Show of Hands:Votes: Party classification:: Votes_Q_02_cluster

---

Date: (Mon) May 30, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

• transform.data chunk:
  • derive features from multiple features
• manage.missing.data chunk:
  • Not fill missing vars
  • Fill missing numerics with a different algorithm
  • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

• Organization:
  • Categorize by chunk
  • Priority criteria:
    0. Ease of change
    1. Impacts report
    2. Cleans innards
    3. Bug report
• all chunks:
  • at chunk-end rm(!glb_)
• manage.missing.data chunk:
  • cleaner way to manage re-splitting of training vs. new entity
• extract.features chunk:
  • Add n-grams for glbFeatsText
    • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
• fit.models chunk:
  • Classification: Plot AUC Curves for all models & highlight glbMdlSel
  • Prediction accuracy scatter graph:
  • Add tiles (raw vs. PCA)
  • Use shiny for drop-down of “important” features

  • Use plot.ly for interactive plots ?

  • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
  • create a custom model for rpart that has minbucket as a tuning parameter

  • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

• Probability handling for multinomials vs. desired binomial outcome
• ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)

• extensions toward multiclass classification are scheduled for the next release

• fit.all.training chunk:
  • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
• Compare glb_sel_mdl vs. glb_fin_mdl:
  • varImp
  • Prediction differences (shd be minimal ?)
• Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
• Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
• Parameterize glb_analytics_pn
• Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
• Do non-glm methods handle interaction terms ?
• f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
• Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
• Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
• Incorporate code chunks in print_sessionInfo
• Test against
  • projects in github.com/bdanalytics
  • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")

## Loading required package: caret

## Loading required package: lattice

## Loading required package: ggplot2

source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)

## Loading required package: plyr

require(dplyr)

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

require(knitr)

## Loading required package: knitr

require(stringr)

## Loading required package: stringr

#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english

## [1] "C/C/C/C/C/en_US.UTF-8"

#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- TRUE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))

#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]])))); 
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)

#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") else    
    glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")

#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
    
glb_preproc_methods <- NULL
#     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Votes_Q_02_cluster_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- "cluster.data" #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #NULL #default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- "data/Votes_Q_01_cnk_manage.missing.data.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#load("Votes_Q_02_cnk_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())

## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))

##          label step_major step_minor label_minor   bgn end elapsed
## 1 cluster.data          1          0           0 8.995  NA      NA

Step 1.0: cluster data

chunk option: eval=

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)} #{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx, keep = c(“glbFeatsCategory”,“glb_dsp_cols”))}

## Loading required package: proxy

## 
## Attaching package: 'proxy'

## The following objects are masked from 'package:stats':
## 
##     as.dist, dist

## The following object is masked from 'package:base':
## 
##     as.matrix

## Loading required package: dynamicTreeCut

## Loading required package: entropy

## Loading required package: tidyr

## Loading required package: ggdendro

## [1] "Clustering features: "
##               abs.cor.y
## Q120472.fctr 0.04620307
## Q98197.fctr  0.05493425
## Q113181.fctr 0.08087531
## Q115611.fctr 0.09044682
## Q109244.fctr 0.12038125
## [1] "    .rnorm cor: -0.0078"
## [1] "  Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6913"

## Loading required package: lazyeval

##   Hhold.fctr .clusterid Hhold.fctr.clusterid    R    D  .entropy .knt
## 1          N          1                  N_1  220  230 0.6929002  450
## 2        MKn          1                MKn_1  308  344 0.6916221  652
## 3        MKy          1                MKy_1  842  752 0.6915524 1594
## 4        PKn          1                PKn_1   49  131 0.5854566  180
## 5        PKy          1                PKy_1   26   35 0.6822232   61
## 6        SKn          1                SKn_1 1091 1340 0.6878923 2431
## 7        SKy          1                SKy_1   81  119 0.6749870  200
## [1] "glbObsAll$Hhold.fctr Entropy: 0.6859 (99.2186 pct)"

## [1] "Category: N"
## [1] "max distance(0.9785) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4459    5563          D          N           NA           NA           NA
## 5038    6295          R          N           No           No           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           No           Pc          Yes          Yes          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           No           No           No           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4459           NA           NA           NA           NA           NA
## 5038      Science          Yes    Try first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4459          Yes       Giving           No          Yes           No
## 5038           No       Giving           No           No           No
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4459          Yes           Pr           No Standard hours   Hot headed
## 5038           NA           NA          Yes Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4459           NA           NA           NA           NA           NA
## 5038           NA           NA           NA           No          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4459           NA           NA           NA           NA           NA
## 5038          Yes           No   Mysterious          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4459           NA           NA           NA          Yes           NA
## 5038        Tunes       People           NA           No           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4459          Yes           No   Supportive           No          Mac
## 5038           No          Yes           NA           NA          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 4459           NA           NA Risk-friendly         Yes!           No
## 5038           NA           NA            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4459        Space           No    In-person           No           No
## 5038           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4459          Yes          Yes           Gr          Yes           No
## 5038           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4459          Yes           No           No          Yes           No
## 5038           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4459          Yes          Yes           No          Yes           No
## 5038           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4459         Rent     Optimist          Mom           No          Yes
## 5038           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4459           No           No          Yes        Nope          No
## 5038           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4459          No          No         Yes          No          No
## 5038          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4459         Yes         Yes         Yes
## 5038          NA          NA          NA
## [1] "min distance(0.9441) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4930    6157          D          N          Yes           NA           NA
## 6569    5059       <NA>          N           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           Pc           No           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4930           NA           NA           NA           NA          Yes
## 6569           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4930           No           No   Mysterious           No           No
## 6569           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4930        Tunes   Technology           NA           NA           NA
## 6569        Tunes   Technology           No           No          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           No           NA           NA           No           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4930          Yes          Yes     Cautious           NA           NA
## 6569           No          Yes           NA       Umm...          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4930           NA           NA           NA           NA           NA
## 6569        Space           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4930           NA           NA           NA           NA           NA
## 6569          Yes          Yes           NA           No           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4930           NA           NA           NA           NA           NA
## 6569           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4930           NA           NA           NA          NA          NA
## 6569           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4930          NA          NA          NA          NA          NA
## 6569          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4930          NA          NA          NA
## 6569          NA          NA          NA
## [1] "Category: MKn"
## [1] "max distance(0.9784) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3547    4420          D        MKn           NA           NA           NA
## 5337    6664          D        MKn          Yes          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           No           Pc          Yes          Yes           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           No          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3547           NA           NA           NA           NA           NA
## 5337      Science          Yes  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3547           NA           NA           NA           NA           NA
## 5337          Yes    Receiving          Yes          Yes           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           Pr           No           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3547           NA           NA          Yes          Yes           No
## 5337           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3547          Yes           No          TMI           No           No
## 5337           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3547        Tunes       People           NA          Yes          Yes
## 5337           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3547           No          Yes    Demanding          Yes           PC
## 5337           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3547           No           NA     Cautious       Umm...           No
## 5337           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3547        Space           No           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3547           NA           NA           NA           NA           NA
## 5337           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3547           No           No           No           No           No
## 5337           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3547          Own     Optimist          Mom          Yes          Yes
## 5337           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3547          Yes           No          Yes      Check!          No
## 5337           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3547          No         Yes         Yes          No          No
## 5337          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3547         Yes          No         Yes
## 5337          NA          NA          NA
## [1] "min distance(0.9373) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2019    2509          D        MKn           NA           NA           NA
## 3363    4185          D        MKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2019           NA          Yes    Demanding           No          Mac
## 3363           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2019          Yes          Yes Risk-friendly           NA           NA
## 3363           NA          Yes            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2019           NA           NA           NA           NA           NA
## 3363           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2019           NA           NA           NA          NA          NA
## 3363           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2019          NA          NA          NA          NA          NA
## 3363          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2019          NA          NA          NA
## 3363          NA          NA          NA
## [1] "Category: MKy"
## [1] "max distance(0.9789) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 6014    2335       <NA>        MKy           NA           NA           NA
## 6304    3742       <NA>        MKy           No          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No           Pc          Yes          Yes           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No          Yes          Yes           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 6014           NA           NA           NA           NA           NA
## 6304      Science          Yes  Study first          Yes          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No       Giving           No          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No           Pr           No    Odd hours           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           No        Happy          Yes          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 6014           NA         P.M.           NA           NA           NA
## 6304           No           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           NA           NA           NA          Yes           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 6014           NA           NA           NA           NA           NA
## 6304           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 6014           NA           NA           NA          Yes          Yes
## 6304           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 6014           No          Yes   Supportive           No           PC
## 6304           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 6014           NA           NA     Cautious       Umm...           No
## 6304           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 6014        Space           No       Online          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 6014          Yes          Yes           Yy          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 6014           No          Yes           No          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 6014          Yes           No           No          Yes           No
## 6304           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 6014          Own    Pessimist          Dad          Yes          Yes
## 6304           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 6014          Yes           No          Yes        Nope          No
## 6304           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 6014          No         Yes          NA          NA          NA
## 6304          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 6014          NA          NA         Yes
## 6304          NA          NA          NA
## [1] "min distance(0.9430) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3335    4153          D        MKy           NA           NA           NA
## 5966    2056       <NA>        MKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3335           NA           NA           NA          Yes          Yes
## 5966           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3335           No          Yes   Mysterious          Yes          Yes
## 5966           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3335         Talk       People           No           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 3335           No          Yes Risk-friendly           NA           NA
## 5966          Yes          Yes            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3335        Space           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3335           NA           NA           NA           NA           NA
## 5966           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3335           NA           NA           NA          NA          NA
## 5966           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3335          NA          NA          NA          NA          NA
## 5966          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3335          NA          NA          NA
## 5966          NA          NA          NA
## [1] "Category: PKn"
## [1] "max distance(0.9770) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2165    2698          R        PKn           NA           NA           NA
## 5415    6762          D        PKn          Yes          Yes           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2165           NA           NA           NA           NA           NA
## 5415           No           Pt           No          Yes          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2165           NA           NA           NA           NA           NA
## 5415          Yes          Yes          Yes           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2165           NA           NA           NA           NA           NA
## 5415      Science          Yes    Try first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2165           NA           NA           NA          Yes           No
## 5415          Yes       Giving          Yes          Yes           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2165           No           Id           No    Odd hours   Hot headed
## 5415           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2165          Yes        Happy           No          Yes           No
## 5415           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2165           NA         A.M.           NA           NA           NA
## 5415           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2165           NA           NA           No          Yes           No
## 5415           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2165          Yes          Yes          TMI          Yes          Yes
## 5415           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2165         Talk   Technology           NA           No          Yes
## 5415           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2165           No          Yes    Demanding          Yes          Mac
## 5415           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2165          Yes           No     Cautious       Umm...           No
## 5415           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2165    Socialize           No       Online           No          Yes
## 5415           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2165          Yes          Yes           Yy          Yes          Yes
## 5415           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2165           No          Yes           No           No           No
## 5415           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2165           No           No           No          Yes           No
## 5415           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2165          Own    Pessimist           NA          Yes          Yes
## 5415           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2165          Yes           No          Yes      Check!          No
## 5415           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2165          No         Yes          NA         Yes         Yes
## 5415          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2165         Yes          No         Yes
## 5415          NA          NA          NA
## [1] "min distance(0.9426) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 848     1046          D        PKn           NA           NA           NA
## 3463    4312          D        PKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA          Yes          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 848           Yes          Yes Risk-friendly         Yes!           No
## 3463          Yes          Yes Risk-friendly         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 848         Space           No    In-person          Yes           NA
## 3463        Space          Yes           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 848            NA           NA           NA           NA           NA
## 3463           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 848            NA           NA           NA          NA          NA
## 3463           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 848           NA          NA          NA          NA          NA
## 3463          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 848           NA          NA          No
## 3463          NA          NA          NA
## [1] "Category: PKy"
## [1] "max distance(0.9776) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1561    1933          R        PKy           NA           NA           NA
## 2346    2921          R        PKy           No           No          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           No           Pc           No           No          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           No          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1561           NA           NA           NA           NA           NA
## 2346      Science          Yes  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1561           NA           NA           NA           NA          Yes
## 2346          Yes       Giving          Yes          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1561          Yes           Pr           No           NA  Cool headed
## 2346          Yes           Id           No    Odd hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1561           No        Happy           No          Yes           No
## 2346           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1561           No         A.M.          Yes        Start          Yes
## 2346           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1561           NA           Cs          Yes           No           No
## 2346           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1561          Yes          Yes   Mysterious           No           No
## 2346           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1561           NA           NA           NA          Yes           No
## 2346           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1561           No           No          Yes           No           NA
## 2346           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1561           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1561           NA           NA           NA           NA          Yes
## 2346           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1561          Yes           No          Yes      Check!          No
## 2346           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1561          No         Yes         Yes          No          No
## 2346          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1561         Yes         Yes         Yes
## 2346          NA          NA          NA
## [1] "min distance(0.9481) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2107    2623          D        PKy           NA           NA           NA
## 6815    6244       <NA>        PKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2107           NA           NA          Yes          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2107          Art           NA           NA          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2107          Yes           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2107           NA           NA           NA           NA          Yes
## 6815           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2107           No           NA           NA          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2107           No          Yes          TMI          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2107         Talk           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2107           NA           NA           NA           NA           PC
## 6815           NA           NA           NA           NA           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2107          Yes          Yes            NA       Umm...           NA
## 6815          Yes          Yes Risk-friendly       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2107           NA          Yes           NA           No           NA
## 6815        Space           No           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2107           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2107           NA           NA           NA          NA          NA
## 6815           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2107          NA          NA          NA          NA          NA
## 6815          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2107          NA          NA          NA
## 6815          NA          NA          NA
## [1] "Category: SKn"
## [1] "max distance(0.9786) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3905    4863          R        SKn           NA           NA           NA
## 4010    4997          D        SKn           NA           NA           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           No           Pt          Yes           No           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3905           NA           NA           NA           NA           NA
## 4010          Yes          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           NA          Yes           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3905           NA           NA           NA           NA           No
## 4010           NA           NA          Yes           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 3905          Yes           Id           No Standard hours  Cool headed
## 4010           NA           Id           No             NA  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3905          Yes        Happy          Yes          Yes          Yes
## 4010           NA           NA           NA          Yes           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3905          Yes         P.M.           NA           NA           NA
## 4010           No         P.M.           NA        Start           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           NA           NA           NA          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           No           No   Mysterious           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3905           NA           NA           NA           NA           NA
## 4010        Tunes   Technology           NA           NA          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3905           NA           NA           NA           NA           NA
## 4010          Yes           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3905           NA           NA           NA           NA           NA
## 4010           NA           NA           NA         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3905           NA           NA           NA           NA           No
## 4010           NA           No    In-person           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3905           No          Yes           Gr          Yes          Yes
## 4010           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3905          Yes          Yes          Yes          Yes           NA
## 4010           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3905           No          Yes          Yes          Yes           No
## 4010           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3905          Own    Pessimist          Mom          Yes          Yes
## 4010           NA     Optimist          Dad           NA           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3905           No           No          Yes        Nope         Yes
## 4010           No          Yes           NA      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3905         Yes         Yes          No          NA          NA
## 4010          No          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3905          NA          NA          NA
## 4010         Yes         Yes         Yes
## [1] "min distance(0.9380) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2524    3142          D        SKn           NA           NA           NA
## 2712    3375          D        SKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2524          Yes          Yes           NA       Umm...           NA
## 2712           No          Yes     Cautious           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2524           NA           No       Online          Yes           NA
## 2712           NA           No           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2524           NA           NA           NA           NA           NA
## 2712           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2524           NA           NA           NA          NA          NA
## 2712           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2524          NA          NA          NA          NA          NA
## 2712          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2524          NA          NA          NA
## 2712          NA          NA          NA
## [1] "Category: SKy"
## [1] "max distance(0.9776) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2213    2759          R        SKy           NA           NA           NA
## 6921    6795       <NA>        SKy          Yes          Yes           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           No           Pc          Yes          Yes          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2213           NA           NA           NA           NA           NA
## 6921          Yes           No          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2213           NA           NA           NA           NA           NA
## 6921      Science          Yes    Try first          Yes          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           No       Giving          Yes          Yes           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2213           NA           NA    Demanding          Yes           PC
## 6921           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2213           NA           NA           NA           NA           NA
## 6921           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2213           NA           NA           NA           NA          Yes
## 6921           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2213          Yes          Yes           Gr          Yes           No
## 6921           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2213          Yes          Yes           No           No           No
## 6921           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2213          Yes          Yes          Yes           No           No
## 6921           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2213          Own    Pessimist          Mom          Yes          Yes
## 6921           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2213          Yes          Yes          Yes        Nope          No
## 6921           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2213         Yes         Yes         Yes          No          No
## 6921          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2213         Yes          No          No
## 6921          NA          NA          NA
## [1] "min distance(0.9466) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2539    3157          R        SKy           NA           NA           NA
## 3850    4797          D        SKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2539           NA           NA           NA           NA           NA
## 3850          Yes           No   Mysterious           No           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2539           NA           NA           NA           NA           NA
## 3850         Talk       People           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2539           NA           No           NA           NA           NA
## 3850           NA           No           NA           No          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2539           NA          Yes            NA           NA           NA
## 3850           NA          Yes Risk-friendly       Umm...          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2539           NA           NA           NA           NA           NA
## 3850        Space          Yes    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           No          Yes           Yy           No           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2539           NA           NA           NA           NA           NA
## 3850          Yes          Yes          Yes           No           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2539           NA           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2539         Rent           NA           NA           NA           NA
## 3850           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2539           NA           NA           NA          NA          NA
## 3850           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2539          NA          NA          NA          NA          NA
## 3850          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2539          NA          NA          NA
## 3850          NA          NA          NA
##    Hhold.fctr .clusterid Hhold.fctr.clusterid   R   D  .entropy .knt
## 1           N          1                  N_1 129 128 0.6931396  257
## 2           N          2                  N_2  54  45 0.6890092   99
## 3           N          3                  N_3  37  57 0.6703386   94
## 4         MKn          1                MKn_1 159 166 0.6929152  325
## 5         MKn          2                MKn_2  31 110 0.5267284  141
## 6         MKn          3                MKn_3  76  43 0.6541879  119
## 7         MKn          4                MKn_4  42  25 0.6606028   67
## 8         MKy          1                MKy_1 560 508 0.6919614 1068
## 9         MKy          2                MKy_2 230 126 0.6498471  356
## 10        MKy          3                MKy_3  52 118 0.6157663  170
## 11        PKn          1                PKn_1  16  30 0.6460905   46
## 12        PKn          2                PKn_2   4  43 0.2910671   47
## 13        PKn          3                PKn_3  16  26 0.6645284   42
## 14        PKn          4                PKn_4  13  32 0.6011538   45
## 15        PKy          1                PKy_1   9  14 0.6693280   23
## 16        PKy          2                PKy_2  13   8 0.6645284   21
## 17        PKy          3                PKy_3   4  13 0.5455946   17
## 18        SKn          1                SKn_1 513 492 0.6929289 1005
## 19        SKn          2                SKn_2 413 452 0.6921304  865
## 20        SKn          3                SKn_3 165 396 0.6057975  561
## 21        SKy          1                SKy_1  43  50 0.6903118   93
## 22        SKy          2                SKy_2  25  25 0.6931472   50
## 23        SKy          3                SKy_3  13  44 0.5369340   57
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.6658 (97.0682 pct)"

##                     label step_major step_minor label_minor     bgn
## 1            cluster.data          1          0           0   8.995
## 2 partition.data.training          2          0           0 131.868
##       end elapsed
## 1 131.868 122.873
## 2      NA      NA

Step 2.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"

## Loading required package: sqldf

## Loading required package: gsubfn

## Loading required package: proto

## Loading required package: RSQLite

## Loading required package: DBI

## Loading required package: tcltk

## Loading required package: reshape2

## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 1.05 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 1.05 secs"

## Loading required package: sampling

## 
## Attaching package: 'sampling'

## The following object is masked from 'package:caret':
## 
##     cluster

## [1] "lclgetMatrixCorrelation: duration: 41.218000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 14.727000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 49.771000 secs"
## [1] "cor of Fit vs. New: 1.0000"

## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 107.38 secs"

##     Party.Democrat Party.Republican Party.NA
##                 NA               NA     1392
## Fit           2357             2091       NA
## OOB            594              526       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.5299011        0.4700989       NA
## OOB      0.5303571        0.4696429       NA
##   Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6        SKn   1920    511    638     0.43165468    0.456250000
## 2        MKy   1296    298    371     0.29136691    0.266071429
## 1        MKn    516    136    169     0.11600719    0.121428571
## 3          N    367     83    102     0.08250899    0.074107143
## 7        SKy    147     53     65     0.03304856    0.047321429
## 4        PKn    150     30     37     0.03372302    0.026785714
## 5        PKy     52      9     10     0.01169065    0.008035714
##   .freqRatio.Tst
## 6    0.458333333
## 2    0.266522989
## 1    0.121408046
## 3    0.073275862
## 7    0.046695402
## 4    0.026580460
## 5    0.007183908

## [1] "glbObsAll: "

## [1] 6960  221

## [1] "glbObsTrn: "

## [1] 5568  221

## [1] "glbObsFit: "

## [1] 4448  220

## [1] "glbObsOOB: "

## [1] 1120  220

## [1] "glbObsNew: "

## [1] 1392  220

## [1] "partition.data.training chunk: teardown: elapsed: 108.33 secs"

##                     label step_major step_minor label_minor     bgn
## 2 partition.data.training          2          0           0 131.868
## 3         select.features          3          0           0 240.288
##       end elapsed
## 2 240.287  108.42
## 3      NA      NA

Step 3.0: select features

##                         cor.y exclude.as.feat    cor.y.abs cor.high.X
## Q109244.fctr     0.1203812469               1 0.1203812469         NA
## .clusterid       0.1012091920               1 0.1012091920         NA
## .clusterid.fctr  0.1012091920               0 0.1012091920         NA
## Hhold.fctr       0.0511386673               0 0.0511386673         NA
## Edn.fctr         0.0359295351               0 0.0359295351         NA
## Q101163.fctr     0.0295046473               1 0.0295046473         NA
## Q100689.fctr     0.0256915080               1 0.0256915080         NA
## Q98078.fctr      0.0256516490               1 0.0256516490         NA
## Q99716.fctr      0.0209286674               1 0.0209286674         NA
## Q120379.fctr     0.0206291292               1 0.0206291292         NA
## Q121699.fctr     0.0196933075               1 0.0196933075         NA
## Q105840.fctr     0.0195569165               1 0.0195569165         NA
## Q113583.fctr     0.0191894717               1 0.0191894717         NA
## Q115195.fctr     0.0174522586               1 0.0174522586         NA
## Q102089.fctr     0.0174087944               1 0.0174087944         NA
## Q98059.fctr      0.0171637755               1 0.0171637755         NA
## Q114386.fctr     0.0168013326               1 0.0168013326         NA
## Q100680.fctr     0.0157762454               1 0.0157762454         NA
## Q108342.fctr     0.0151842510               1 0.0151842510         NA
## Q111848.fctr     0.0141099384               1 0.0141099384         NA
## YOB.Age.fctr     0.0129198495               0 0.0129198495         NA
## Q118892.fctr     0.0125250379               1 0.0125250379         NA
## Q102687.fctr     0.0120079165               1 0.0120079165         NA
## Q115390.fctr     0.0119300319               1 0.0119300319         NA
## Q119851.fctr     0.0093381833               1 0.0093381833         NA
## Q114517.fctr     0.0084741753               1 0.0084741753         NA
## Q120012.fctr     0.0084652930               1 0.0084652930         NA
## Q109367.fctr     0.0080456026               1 0.0080456026         NA
## Q114961.fctr     0.0079206587               1 0.0079206587         NA
## Q121700.fctr     0.0067756198               1 0.0067756198         NA
## Q124122.fctr     0.0061257448               1 0.0061257448         NA
## Q111220.fctr     0.0055758571               1 0.0055758571         NA
## Q113992.fctr     0.0041479796               1 0.0041479796         NA
## Q121011.fctr     0.0037329030               1 0.0037329030         NA
## Q106042.fctr     0.0032327194               1 0.0032327194         NA
## Q116448.fctr     0.0031731051               1 0.0031731051         NA
## Q116601.fctr     0.0022379241               1 0.0022379241         NA
## Q104996.fctr     0.0012202806               1 0.0012202806         NA
## Q102906.fctr     0.0011540297               1 0.0011540297         NA
## Q113584.fctr     0.0011387024               1 0.0011387024         NA
## Q108950.fctr     0.0010567028               1 0.0010567028         NA
## Q102674.fctr     0.0009759844               1 0.0009759844         NA
## Q103293.fctr     0.0005915534               1 0.0005915534         NA
## Q112478.fctr     0.0001517248               1 0.0001517248         NA
## Q114748.fctr    -0.0008477228               1 0.0008477228         NA
## Q107491.fctr    -0.0014031814               1 0.0014031814         NA
## Q100562.fctr    -0.0017132769               1 0.0017132769         NA
## Q108617.fctr    -0.0024119725               1 0.0024119725         NA
## Q100010.fctr    -0.0024291540               1 0.0024291540         NA
## Q115602.fctr    -0.0027844465               1 0.0027844465         NA
## Q116953.fctr    -0.0029786716               1 0.0029786716         NA
## Q115610.fctr    -0.0035255582               1 0.0035255582         NA
## Q106997.fctr    -0.0041749086               1 0.0041749086         NA
## Q120978.fctr    -0.0044187616               1 0.0044187616         NA
## Q112512.fctr    -0.0056768212               1 0.0056768212         NA
## Q108343.fctr    -0.0060665340               1 0.0060665340         NA
## Q96024.fctr     -0.0069116541               1 0.0069116541         NA
## Q106389.fctr    -0.0077498918               1 0.0077498918         NA
## .rnorm          -0.0078039520               0 0.0078039520         NA
## Q108754.fctr    -0.0080847764               1 0.0080847764         NA
## Q98578.fctr     -0.0081164509               1 0.0081164509         NA
## Q101162.fctr    -0.0099412952               1 0.0099412952         NA
## Q115777.fctr    -0.0101315203               1 0.0101315203         NA
## Q99581.fctr     -0.0103662478               1 0.0103662478         NA
## Q124742.fctr    -0.0111642906               1 0.0111642906         NA
## Q116797.fctr    -0.0112749656               1 0.0112749656         NA
## Q112270.fctr    -0.0116157798               1 0.0116157798         NA
## YOB             -0.0116828198               1 0.0116828198         NA
## Q118237.fctr    -0.0117079669               1 0.0117079669         NA
## Q119650.fctr    -0.0125645475               1 0.0125645475         NA
## Q111580.fctr    -0.0132382335               1 0.0132382335         NA
## Q123464.fctr    -0.0136140083               1 0.0136140083         NA
## Q117193.fctr    -0.0138241599               1 0.0138241599         NA
## Q99982.fctr     -0.0139727928               1 0.0139727928         NA
## Q108856.fctr    -0.0140363785               1 0.0140363785         NA
## Q118233.fctr    -0.0147269325               1 0.0147269325         NA
## Q102289.fctr    -0.0155850393               1 0.0155850393         NA
## Q116197.fctr    -0.0158561766               1 0.0158561766         NA
## Income.fctr     -0.0159635458               0 0.0159635458         NA
## Q118232.fctr    -0.0171321152               1 0.0171321152         NA
## Q120194.fctr    -0.0172986920               1 0.0172986920         NA
## Q114152.fctr    -0.0175013163               1 0.0175013163         NA
## Q122770.fctr    -0.0194639697               1 0.0194639697         NA
## Q117186.fctr    -0.0198853672               1 0.0198853672         NA
## Q105655.fctr    -0.0198994078               1 0.0198994078         NA
## Q106993.fctr    -0.0207428635               1 0.0207428635         NA
## Q119334.fctr    -0.0226894034               1 0.0226894034         NA
## Q122120.fctr    -0.0229287700               1 0.0229287700         NA
## Q116441.fctr    -0.0237358205               1 0.0237358205         NA
## Q118117.fctr    -0.0253544150               1 0.0253544150         NA
## Q123621.fctr    -0.0255329743               1 0.0255329743         NA
## Q122769.fctr    -0.0259739146               1 0.0259739146         NA
## Q120650.fctr    -0.0270889067               1 0.0270889067         NA
## Q98869.fctr     -0.0276734114               1 0.0276734114         NA
## .pos            -0.0302037138               1 0.0302037138         NA
## USER_ID         -0.0302304868               1 0.0302304868         NA
## Q107869.fctr    -0.0304661021               1 0.0304661021         NA
## Q120014.fctr    -0.0318620439               1 0.0318620439         NA
## Q115899.fctr    -0.0324177950               1 0.0324177950         NA
## Q106388.fctr    -0.0341579350               1 0.0341579350         NA
## Q99480.fctr     -0.0344412239               1 0.0344412239         NA
## Q122771.fctr    -0.0348421015               1 0.0348421015         NA
## Q108855.fctr    -0.0370970211               1 0.0370970211         NA
## Q110740.fctr    -0.0380691243               1 0.0380691243         NA
## Q106272.fctr    -0.0400926462               1 0.0400926462         NA
## Q101596.fctr    -0.0409784077               1 0.0409784077         NA
## Q116881.fctr    -0.0416860293               1 0.0416860293         NA
## Q120472.fctr    -0.0462030674               1 0.0462030674         NA
## Q98197.fctr     -0.0549342527               1 0.0549342527         NA
## Q113181.fctr    -0.0808753072               1 0.0808753072         NA
## Q115611.fctr    -0.0904468203               1 0.0904468203         NA
## Gender.fctr     -0.1027400851               0 0.1027400851         NA
##                 freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## Q109244.fctr     1.125916    0.05387931   FALSE FALSE            FALSE
## .clusterid       1.784041    0.07183908   FALSE FALSE            FALSE
## .clusterid.fctr  1.784041    0.07183908   FALSE FALSE            FALSE
## Hhold.fctr       1.525094    0.12571839   FALSE FALSE            FALSE
## Edn.fctr         1.392610    0.14367816   FALSE FALSE            FALSE
## Q101163.fctr     1.327394    0.05387931   FALSE FALSE            FALSE
## Q100689.fctr     1.029800    0.05387931   FALSE FALSE            FALSE
## Q98078.fctr      1.266595    0.05387931   FALSE FALSE            FALSE
## Q99716.fctr      1.328693    0.05387931   FALSE FALSE            FALSE
## Q120379.fctr     1.046326    0.05387931   FALSE FALSE            FALSE
## Q121699.fctr     1.507127    0.05387931   FALSE FALSE            FALSE
## Q105840.fctr     1.275362    0.05387931   FALSE FALSE            FALSE
## Q113583.fctr     1.102515    0.05387931   FALSE FALSE            FALSE
## Q115195.fctr     1.065496    0.05387931   FALSE FALSE            FALSE
## Q102089.fctr     1.055963    0.05387931   FALSE FALSE            FALSE
## Q98059.fctr      1.493810    0.05387931   FALSE FALSE            FALSE
## Q114386.fctr     1.092072    0.05387931   FALSE FALSE            FALSE
## Q100680.fctr     1.102386    0.05387931   FALSE FALSE            FALSE
## Q108342.fctr     1.048292    0.05387931   FALSE FALSE            FALSE
## Q111848.fctr     1.113602    0.05387931   FALSE FALSE            FALSE
## YOB.Age.fctr     1.005794    0.16163793   FALSE FALSE            FALSE
## Q118892.fctr     1.347380    0.05387931   FALSE FALSE            FALSE
## Q102687.fctr     1.256545    0.05387931   FALSE FALSE            FALSE
## Q115390.fctr     1.150505    0.05387931   FALSE FALSE            FALSE
## Q119851.fctr     1.244519    0.05387931   FALSE FALSE            FALSE
## Q114517.fctr     1.183374    0.05387931   FALSE FALSE            FALSE
## Q120012.fctr     1.047185    0.05387931   FALSE FALSE            FALSE
## Q109367.fctr     1.008571    0.05387931   FALSE FALSE            FALSE
## Q114961.fctr     1.250436    0.05387931   FALSE FALSE            FALSE
## Q121700.fctr     1.708221    0.05387931   FALSE FALSE             TRUE
## Q124122.fctr     1.412807    0.05387931   FALSE FALSE             TRUE
## Q111220.fctr     1.262849    0.05387931   FALSE FALSE             TRUE
## Q113992.fctr     1.267442    0.05387931   FALSE FALSE             TRUE
## Q121011.fctr     1.153676    0.05387931   FALSE FALSE             TRUE
## Q106042.fctr     1.247738    0.05387931   FALSE FALSE             TRUE
## Q116448.fctr     1.161031    0.05387931   FALSE FALSE             TRUE
## Q116601.fctr     1.394914    0.05387931   FALSE FALSE             TRUE
## Q104996.fctr     1.173840    0.05387931   FALSE FALSE             TRUE
## Q102906.fctr     1.053396    0.05387931   FALSE FALSE             TRUE
## Q113584.fctr     1.212486    0.05387931   FALSE FALSE             TRUE
## Q108950.fctr     1.103872    0.05387931   FALSE FALSE             TRUE
## Q102674.fctr     1.073412    0.05387931   FALSE FALSE             TRUE
## Q103293.fctr     1.122287    0.05387931   FALSE FALSE             TRUE
## Q112478.fctr     1.113648    0.05387931   FALSE FALSE             TRUE
## Q114748.fctr     1.051125    0.05387931   FALSE FALSE             TRUE
## Q107491.fctr     1.419021    0.05387931   FALSE FALSE             TRUE
## Q100562.fctr     1.217215    0.05387931   FALSE FALSE             TRUE
## Q108617.fctr     1.390618    0.05387931   FALSE FALSE             TRUE
## Q100010.fctr     1.268156    0.05387931   FALSE FALSE             TRUE
## Q115602.fctr     1.322302    0.05387931   FALSE FALSE             TRUE
## Q116953.fctr     1.039180    0.05387931   FALSE FALSE             TRUE
## Q115610.fctr     1.359695    0.05387931   FALSE FALSE             TRUE
## Q106997.fctr     1.177632    0.05387931   FALSE FALSE             TRUE
## Q120978.fctr     1.131963    0.05387931   FALSE FALSE             TRUE
## Q112512.fctr     1.299253    0.05387931   FALSE FALSE             TRUE
## Q108343.fctr     1.064910    0.05387931   FALSE FALSE             TRUE
## Q96024.fctr      1.144428    0.05387931   FALSE FALSE             TRUE
## Q106389.fctr     1.341307    0.05387931   FALSE FALSE             TRUE
## .rnorm           1.000000  100.00000000   FALSE FALSE            FALSE
## Q108754.fctr     1.008090    0.05387931   FALSE FALSE            FALSE
## Q98578.fctr      1.093556    0.05387931   FALSE FALSE            FALSE
## Q101162.fctr     1.103229    0.05387931   FALSE FALSE            FALSE
## Q115777.fctr     1.140288    0.05387931   FALSE FALSE            FALSE
## Q99581.fctr      1.375000    0.05387931   FALSE FALSE            FALSE
## Q124742.fctr     2.565379    0.05387931   FALSE FALSE            FALSE
## Q116797.fctr     1.009589    0.05387931   FALSE FALSE            FALSE
## Q112270.fctr     1.254284    0.05387931   FALSE FALSE            FALSE
## YOB              1.027559    1.41882184   FALSE FALSE            FALSE
## Q118237.fctr     1.088017    0.05387931   FALSE FALSE            FALSE
## Q119650.fctr     1.456978    0.05387931   FALSE FALSE            FALSE
## Q111580.fctr     1.024977    0.05387931   FALSE FALSE            FALSE
## Q123464.fctr     1.326681    0.05387931   FALSE FALSE            FALSE
## Q117193.fctr     1.140665    0.05387931   FALSE FALSE            FALSE
## Q99982.fctr      1.339380    0.05387931   FALSE FALSE            FALSE
## Q108856.fctr     1.080645    0.05387931   FALSE FALSE            FALSE
## Q118233.fctr     1.199142    0.05387931   FALSE FALSE            FALSE
## Q102289.fctr     1.033482    0.05387931   FALSE FALSE            FALSE
## Q116197.fctr     1.073778    0.05387931   FALSE FALSE            FALSE
## Income.fctr      1.256724    0.12571839   FALSE FALSE            FALSE
## Q118232.fctr     1.365812    0.05387931   FALSE FALSE            FALSE
## Q120194.fctr     1.016716    0.05387931   FALSE FALSE            FALSE
## Q114152.fctr     1.027617    0.05387931   FALSE FALSE            FALSE
## Q122770.fctr     1.008802    0.05387931   FALSE FALSE            FALSE
## Q117186.fctr     1.053878    0.05387931   FALSE FALSE            FALSE
## Q105655.fctr     1.079316    0.05387931   FALSE FALSE            FALSE
## Q106993.fctr     1.327392    0.05387931   FALSE FALSE            FALSE
## Q119334.fctr     1.081498    0.05387931   FALSE FALSE            FALSE
## Q122120.fctr     1.297443    0.05387931   FALSE FALSE            FALSE
## Q116441.fctr     1.019645    0.05387931   FALSE FALSE            FALSE
## Q118117.fctr     1.174006    0.05387931   FALSE FALSE            FALSE
## Q123621.fctr     1.466381    0.05387931   FALSE FALSE            FALSE
## Q122769.fctr     1.060606    0.05387931   FALSE FALSE            FALSE
## Q120650.fctr     1.896247    0.05387931   FALSE FALSE            FALSE
## Q98869.fctr      1.080860    0.05387931   FALSE FALSE            FALSE
## .pos             1.000000  100.00000000   FALSE FALSE            FALSE
## USER_ID          1.000000  100.00000000   FALSE FALSE            FALSE
## Q107869.fctr     1.211050    0.05387931   FALSE FALSE            FALSE
## Q120014.fctr     1.044944    0.05387931   FALSE FALSE            FALSE
## Q115899.fctr     1.197849    0.05387931   FALSE FALSE            FALSE
## Q106388.fctr     1.065033    0.05387931   FALSE FALSE            FALSE
## Q99480.fctr      1.225404    0.05387931   FALSE FALSE            FALSE
## Q122771.fctr     1.414753    0.05387931   FALSE FALSE            FALSE
## Q108855.fctr     1.273980    0.05387931   FALSE FALSE            FALSE
## Q110740.fctr     1.050779    0.05387931   FALSE FALSE            FALSE
## Q106272.fctr     1.116536    0.05387931   FALSE FALSE            FALSE
## Q101596.fctr     1.041667    0.05387931   FALSE FALSE            FALSE
## Q116881.fctr     1.010066    0.05387931   FALSE FALSE            FALSE
## Q120472.fctr     1.292633    0.05387931   FALSE FALSE            FALSE
## Q98197.fctr      1.129371    0.05387931   FALSE FALSE            FALSE
## Q113181.fctr     1.006354    0.05387931   FALSE FALSE            FALSE
## Q115611.fctr     1.194859    0.05387931   FALSE FALSE            FALSE
## Gender.fctr      1.561033    0.05387931   FALSE FALSE            FALSE

## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## [1] cor.y            exclude.as.feat  cor.y.abs        cor.high.X      
## [5] freqRatio        percentUnique    zeroVar          nzv             
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)

## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in glbObsAll: "
##        YOB Party.fctr 
##        415       1392 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024            .lcn 
##            2836            2858            1392

## [1] "glb_feats_df:"

## [1] 112  12

##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE

##                    id       cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID -0.03023049            TRUE 0.03023049         NA
## Party.fctr Party.fctr          NA            TRUE         NA         NA
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                <NA>                   NA       FALSE   TRUE
## Party.fctr             <NA>                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE

## [1] "glb_feats_df vs. glbObsAll: "

## character(0)

## [1] "glbObsAll vs. glb_feats_df: "

## character(0)

##             label step_major step_minor label_minor     bgn     end
## 3 select.features          3          0           0 240.288 243.474
## 4      fit.models          4          0           0 243.475      NA
##   elapsed
## 3   3.186
## 4      NA

Step 4.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 244.086  NA      NA

# load(paste0(glbOut$pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 244.086 244.118
## 2 fit.models_0_MFO          1          1 myMFO_classfr 244.119      NA
##   elapsed
## 1   0.032
## 2      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.439000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
##         D         R 
## 0.5299011 0.4700989 
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.973000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.975000 secs"

## Loading required namespace: pROC

## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"

## Loading required package: ROCR

## Loading required package: gplots

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 6.026000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.523
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            0            1
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.5       0.6395473        0.4700989
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4553427             0.4848945             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            0            1             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5       0.6391252        0.4696429
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4400805             0.4993651             0
## [1] "myfit_mdl: exit: 6.036000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 244.119
## 3 fit.models_0_Random          1          2 myrandom_classfr 250.160
##      end elapsed
## 2 250.16   6.041
## 3     NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.427000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.708000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.709000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 6.865000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.274                 0.003       0.4942483
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4619799    0.5265168       0.5073101                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6395473        0.4700989             0.4553427
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.4848945             0        0.523569          0.5
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.547138       0.5191202                   0.55       0.6391252
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.4696429             0.4400805             0.4993651
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 6.882000 secs"

# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")

##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 250.160 257.053   6.893
## 4 257.054      NA      NA

ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Gender.fctr,.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.686000 secs"

## Loading required package: glmnet

## Loading required package: Matrix

## 
## Attaching package: 'Matrix'

## The following object is masked from 'package:tidyr':
## 
##     expand

## Loaded glmnet 2.0-5

## Fitting alpha = 0.1, lambda = 0.00133 on full training set
## [1] "myfit_mdl: train complete: 1.494000 secs"

##             Length Class      Mode     
## a0           51    -none-     numeric  
## beta        255    dgCMatrix  S4       
## df           51    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       51    -none-     numeric  
## dev.ratio    51    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        5    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##      (Intercept) .clusterid.fctr2 .clusterid.fctr3 .clusterid.fctr4 
##       0.15317872       0.08327614       0.66609212       0.15318789 
##     Gender.fctrF     Gender.fctrM 
##       0.04366121      -0.32825671 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"      ".clusterid.fctr2" ".clusterid.fctr3"
## [4] ".clusterid.fctr4" "Gender.fctrF"     "Gender.fctrM"    
## [1] "myfit_mdl: train diagnostics complete: 1.599000 secs"

##          Prediction
## Reference    R    D
##         R 1964  127
##         D 2059  298
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.50854317     0.06243999     0.49373750     0.52333760     0.52990108 
## AccuracyPValue  McnemarPValue 
##     0.99792950     0.00000000

##          Prediction
## Reference   R   D
##         R 498  28
##         D 509  85
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.205357e-01   8.539590e-02   4.908101e-01   5.501532e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   7.545313e-01   2.615744e-95 
## [1] "myfit_mdl: predict complete: 5.835000 secs"
##                           id                       feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Gender.fctr,.clusterid.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.799                 0.059       0.5701775
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5820182    0.5583369       0.4116226                    0.7
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6424599        0.5085432             0.4937375
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5233376    0.06243999       0.5575719    0.5646388
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.5505051       0.4278671                    0.7       0.6497065
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5205357             0.4908101             0.5501532
##   max.Kappa.OOB
## 1     0.0853959
## [1] "myfit_mdl: exit: 5.848000 secs"

if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Gender.fctr,.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.700000 secs"

## Loading required package: rpart

## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.021 on full training set
## [1] "myfit_mdl: train complete: 2.319000 secs"

## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 4448 
## 
##           CP nsplit rel error
## 1 0.04208513      0 1.0000000
## 2 0.00000000      2 0.9158297
## 
## Variable importance
## .clusterid.fctr3     Gender.fctrM     Gender.fctrF 
##               55               23               22 
## 
## Node number 1: 4448 observations,    complexity param=0.04208513
##   predicted class=D  expected loss=0.4700989  P(node) =1
##     class counts:  2091  2357
##    probabilities: 0.470 0.530 
##   left son=2 (3616 obs) right son=3 (832 obs)
##   Primary splits:
##       .clusterid.fctr3 < 0.5 to the left,  improve=3.918880e+01, (0 missing)
##       Gender.fctrM     < 0.5 to the right, improve=2.439999e+01, (0 missing)
##       Gender.fctrF     < 0.5 to the left,  improve=2.265952e+01, (0 missing)
##       .clusterid.fctr2 < 0.5 to the right, improve=1.449171e+00, (0 missing)
##       .clusterid.fctr4 < 0.5 to the left,  improve=7.788846e-04, (0 missing)
## 
## Node number 2: 3616 observations,    complexity param=0.04208513
##   predicted class=R  expected loss=0.4980642  P(node) =0.8129496
##     class counts:  1815  1801
##    probabilities: 0.502 0.498 
##   left son=4 (2258 obs) right son=5 (1358 obs)
##   Primary splits:
##       Gender.fctrM     < 0.5 to the right, improve=16.4948000, (0 missing)
##       Gender.fctrF     < 0.5 to the left,  improve=14.3451000, (0 missing)
##       .clusterid.fctr2 < 0.5 to the left,  improve= 0.5081298, (0 missing)
##       .clusterid.fctr4 < 0.5 to the left,  improve= 0.2211735, (0 missing)
##   Surrogate splits:
##       Gender.fctrF < 0.5 to the left,  agree=0.978, adj=0.94, (0 split)
## 
## Node number 3: 832 observations
##   predicted class=D  expected loss=0.3317308  P(node) =0.1870504
##     class counts:   276   556
##    probabilities: 0.332 0.668 
## 
## Node number 4: 2258 observations
##   predicted class=R  expected loss=0.4610275  P(node) =0.5076439
##     class counts:  1217  1041
##    probabilities: 0.539 0.461 
## 
## Node number 5: 1358 observations
##   predicted class=D  expected loss=0.4403535  P(node) =0.3053058
##     class counts:   598   760
##    probabilities: 0.440 0.560 
## 
## n= 4448 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 4448 2091 D (0.4700989 0.5299011)  
##   2) .clusterid.fctr3< 0.5 3616 1801 R (0.5019358 0.4980642)  
##     4) Gender.fctrM>=0.5 2258 1041 R (0.5389725 0.4610275) *
##     5) Gender.fctrM< 0.5 1358  598 D (0.4403535 0.5596465) *
##   3) .clusterid.fctr3>=0.5 832  276 D (0.3317308 0.6682692) *
## [1] "myfit_mdl: train diagnostics complete: 3.132000 secs"

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 7.496000 secs"
##                     id                       feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Gender.fctr,.clusterid.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.611                 0.018       0.5701775
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5820182    0.5583369       0.4173716                    0.7
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6395473        0.5694703             0.4553427
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.4848945     0.1397342       0.5575719    0.5646388
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.5505051        0.436283                    0.7       0.6391252
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.4696429             0.4400805             0.4993651
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0          0.0106117      0.02100317
## [1] "myfit_mdl: exit: 7.511000 secs"

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")

##                            label step_major step_minor label_minor     bgn
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet 257.054
## 5         fit.models_0_Low.cor.X          1          4      glmnet 270.457
##       end elapsed
## 4 270.457  13.403
## 5      NA      NA

indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.715000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00618 on full training set
## [1] "myfit_mdl: train complete: 4.867000 secs"

##             Length Class      Mode     
## a0            58   -none-     numeric  
## beta        2958   dgCMatrix  S4       
## df            58   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        58   -none-     numeric  
## dev.ratio     58   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        51   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                    (Intercept)                         .rnorm 
##                   0.2191930523                  -0.0001578696 
##                     Edn.fctr.L                     Edn.fctr^4 
##                   0.0227319937                  -0.1825119570 
##                     Edn.fctr^5                     Edn.fctr^7 
##                  -0.0313213205                   0.1472718213 
##                   Gender.fctrM                  Hhold.fctrMKn 
##                  -0.2768838131                  -0.0033414146 
##                  Hhold.fctrMKy                  Hhold.fctrPKn 
##                  -0.2218131392                   0.6449818135 
##                  Hhold.fctrSKy                  Income.fctr.Q 
##                   0.0939556740                  -0.0647543426 
##                  Income.fctr.C                 YOB.Age.fctr.L 
##                  -0.1298650683                   0.2117746741 
##                 YOB.Age.fctr.Q                 YOB.Age.fctr^4 
##                   0.0649495145                   0.0877950477 
##                 YOB.Age.fctr^6                 YOB.Age.fctr^7 
##                   0.0694562859                  -0.0767162362 
##                 YOB.Age.fctr^8 Hhold.fctrMKn:.clusterid.fctr2 
##                  -0.1071714987                   1.0204049056 
## Hhold.fctrMKy:.clusterid.fctr2 Hhold.fctrPKn:.clusterid.fctr2 
##                  -0.3737679058                   1.2430889164 
## Hhold.fctrPKy:.clusterid.fctr2   Hhold.fctrN:.clusterid.fctr3 
##                  -0.0346131753                   0.1874531773 
## Hhold.fctrMKn:.clusterid.fctr3 Hhold.fctrMKy:.clusterid.fctr3 
##                  -0.5640516064                   0.7409635659 
## Hhold.fctrPKy:.clusterid.fctr3 Hhold.fctrSKn:.clusterid.fctr3 
##                   0.5582357714                   0.8358309089 
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr4 
##                   0.7109593830                  -0.3408243730 
## Hhold.fctrPKn:.clusterid.fctr4 
##                   0.1620282232 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                         .rnorm 
##                    0.224848967                   -0.002034718 
##                     Edn.fctr.L                     Edn.fctr^4 
##                    0.022961291                   -0.194514138 
##                     Edn.fctr^5                     Edn.fctr^7 
##                   -0.036840793                    0.155379148 
##                   Gender.fctrM                  Hhold.fctrMKn 
##                   -0.279933696                   -0.016301531 
##                  Hhold.fctrMKy                  Hhold.fctrPKn 
##                   -0.231905525                    0.642479758 
##                  Hhold.fctrSKy                  Income.fctr.Q 
##                    0.095893072                   -0.070064887 
##                  Income.fctr.C                 YOB.Age.fctr.L 
##                   -0.135092466                    0.230026983 
##                 YOB.Age.fctr.Q                 YOB.Age.fctr^4 
##                    0.076434784                    0.098777478 
##                 YOB.Age.fctr^5                 YOB.Age.fctr^6 
##                    0.002459613                    0.077008267 
##                 YOB.Age.fctr^7                 YOB.Age.fctr^8 
##                   -0.082901882                   -0.113404386 
## Hhold.fctrMKn:.clusterid.fctr2 Hhold.fctrMKy:.clusterid.fctr2 
##                    1.040824952                   -0.377065873 
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrPKy:.clusterid.fctr2 
##                    1.283976001                   -0.061655569 
##   Hhold.fctrN:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr3 
##                    0.201531092                   -0.571036695 
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr3 
##                    0.754435537                    0.583943428 
## Hhold.fctrSKn:.clusterid.fctr3 Hhold.fctrSKy:.clusterid.fctr3 
##                    0.841193304                    0.727138575 
## Hhold.fctrMKn:.clusterid.fctr4 Hhold.fctrPKn:.clusterid.fctr4 
##                   -0.349784240                    0.186310773 
## [1] "myfit_mdl: train diagnostics complete: 5.523000 secs"

##          Prediction
## Reference    R    D
##         R 1897  194
##         D 1751  606
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.627248e-01   1.577017e-01   5.479974e-01   5.773698e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   5.988238e-06  1.119841e-272

##          Prediction
## Reference   R   D
##         R 518   8
##         D 556  38
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.964286e-01   4.601965e-02   4.667425e-01   5.261334e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.893662e-01  2.185099e-117 
## [1] "myfit_mdl: predict complete: 12.586000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                        feats
## 1 Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      4.133                 0.231
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1        0.602811    0.6001913    0.6054306       0.3516187
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.65       0.6610908        0.5891041
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5479974             0.5773698     0.1762674
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5741893    0.5608365    0.5875421       0.4088125
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.75          0.6475        0.4964286
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4667425             0.5261334    0.04601965
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01205845      0.02559737
## [1] "myfit_mdl: exit: 12.600000 secs"

fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")

##                    label step_major step_minor label_minor     bgn     end
## 5 fit.models_0_Low.cor.X          1          4      glmnet 270.457 283.111
## 6       fit.models_0_end          1          5    teardown 283.112      NA
##   elapsed
## 5  12.654
## 6      NA

rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)

##        label step_major step_minor label_minor     bgn     end elapsed
## 4 fit.models          4          0           0 243.475 283.124   39.65
## 5 fit.models          4          1           1 283.125      NA      NA

fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 286.658  NA      NA

# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
    fit.models_1_chunk_df <- 
        myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
                    major.inc = FALSE, label.minor = "setup")

    indepVar <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, 
                                    grepl(".glmnet", id, fixed = TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select important features
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glbFeatsInteractionOnly)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      imp > imp_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
            indepVar <- setdiff(indepVar, topindep_var)
            if (length(interact_vars) > 0) {
                indepVar <- 
                    setdiff(indepVar, myextract_actual_feats(interact_vars))
                indepVar <- c(indepVar, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indepVar <- union(indepVar, topindep_var)
        }
    }
    
    if (is.null(indepVar))
        indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]

    if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
        indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indepVar))
        indepVar <- mygetIndepVar(glb_feats_df)
    
    if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {    
        indepVar <- 
            eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
    }    

    indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
            if (!is.null(glbMdlFamilies[["Best.Interact"]]))
                glbMdlFamilies[[mdl_id_pfx]] <-
                    glbMdlFamilies[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    } else fitobs_df <- glbObsFit

    if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indepVar <- setdiff(indepVar, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)

        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,
            trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indepVar = indepVar, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glbObsOOB)
        
#         ntv_mdl <- glmnet(x = as.matrix(
#                               fitobs_df[, indepVar]), 
#                           y = as.factor(as.character(
#                               fitobs_df[, glb_rsp_var])),
#                           family = "multinomial")
#         bgn = 1; end = 100;
#         ntv_mdl <- glmnet(x = as.matrix(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]), 
#                           y = as.factor(as.character(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
#                           family = "multinomial")
    }
}

##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 286.658 286.669
## 2 fit.models_1_All.X          1          1       setup 286.669      NA
##   elapsed
## 1   0.011
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 286.669 286.677
## 3 fit.models_1_All.X          1          2      glmnet 286.677      NA
##   elapsed
## 2   0.008
## 3      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.702000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00618 on full training set
## [1] "myfit_mdl: train complete: 4.977000 secs"

##             Length Class      Mode     
## a0            58   -none-     numeric  
## beta        2958   dgCMatrix  S4       
## df            58   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        58   -none-     numeric  
## dev.ratio     58   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        51   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                    (Intercept)                         .rnorm 
##                   0.2191930523                  -0.0001578696 
##                     Edn.fctr.L                     Edn.fctr^4 
##                   0.0227319937                  -0.1825119570 
##                     Edn.fctr^5                     Edn.fctr^7 
##                  -0.0313213205                   0.1472718213 
##                   Gender.fctrM                  Hhold.fctrMKn 
##                  -0.2768838131                  -0.0033414146 
##                  Hhold.fctrMKy                  Hhold.fctrPKn 
##                  -0.2218131392                   0.6449818135 
##                  Hhold.fctrSKy                  Income.fctr.Q 
##                   0.0939556740                  -0.0647543426 
##                  Income.fctr.C                 YOB.Age.fctr.L 
##                  -0.1298650683                   0.2117746741 
##                 YOB.Age.fctr.Q                 YOB.Age.fctr^4 
##                   0.0649495145                   0.0877950477 
##                 YOB.Age.fctr^6                 YOB.Age.fctr^7 
##                   0.0694562859                  -0.0767162362 
##                 YOB.Age.fctr^8 Hhold.fctrMKn:.clusterid.fctr2 
##                  -0.1071714987                   1.0204049056 
## Hhold.fctrMKy:.clusterid.fctr2 Hhold.fctrPKn:.clusterid.fctr2 
##                  -0.3737679058                   1.2430889164 
## Hhold.fctrPKy:.clusterid.fctr2   Hhold.fctrN:.clusterid.fctr3 
##                  -0.0346131753                   0.1874531773 
## Hhold.fctrMKn:.clusterid.fctr3 Hhold.fctrMKy:.clusterid.fctr3 
##                  -0.5640516064                   0.7409635659 
## Hhold.fctrPKy:.clusterid.fctr3 Hhold.fctrSKn:.clusterid.fctr3 
##                   0.5582357714                   0.8358309089 
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr4 
##                   0.7109593830                  -0.3408243730 
## Hhold.fctrPKn:.clusterid.fctr4 
##                   0.1620282232 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                         .rnorm 
##                    0.224848967                   -0.002034718 
##                     Edn.fctr.L                     Edn.fctr^4 
##                    0.022961291                   -0.194514138 
##                     Edn.fctr^5                     Edn.fctr^7 
##                   -0.036840793                    0.155379148 
##                   Gender.fctrM                  Hhold.fctrMKn 
##                   -0.279933696                   -0.016301531 
##                  Hhold.fctrMKy                  Hhold.fctrPKn 
##                   -0.231905525                    0.642479758 
##                  Hhold.fctrSKy                  Income.fctr.Q 
##                    0.095893072                   -0.070064887 
##                  Income.fctr.C                 YOB.Age.fctr.L 
##                   -0.135092466                    0.230026983 
##                 YOB.Age.fctr.Q                 YOB.Age.fctr^4 
##                    0.076434784                    0.098777478 
##                 YOB.Age.fctr^5                 YOB.Age.fctr^6 
##                    0.002459613                    0.077008267 
##                 YOB.Age.fctr^7                 YOB.Age.fctr^8 
##                   -0.082901882                   -0.113404386 
## Hhold.fctrMKn:.clusterid.fctr2 Hhold.fctrMKy:.clusterid.fctr2 
##                    1.040824952                   -0.377065873 
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrPKy:.clusterid.fctr2 
##                    1.283976001                   -0.061655569 
##   Hhold.fctrN:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr3 
##                    0.201531092                   -0.571036695 
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr3 
##                    0.754435537                    0.583943428 
## Hhold.fctrSKn:.clusterid.fctr3 Hhold.fctrSKy:.clusterid.fctr3 
##                    0.841193304                    0.727138575 
## Hhold.fctrMKn:.clusterid.fctr4 Hhold.fctrPKn:.clusterid.fctr4 
##                   -0.349784240                    0.186310773 
## [1] "myfit_mdl: train diagnostics complete: 5.697000 secs"

##          Prediction
## Reference    R    D
##         R 1897  194
##         D 1751  606
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.627248e-01   1.577017e-01   5.479974e-01   5.773698e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   5.988238e-06  1.119841e-272

##          Prediction
## Reference   R   D
##         R 518   8
##         D 556  38
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.964286e-01   4.601965e-02   4.667425e-01   5.261334e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.893662e-01  2.185099e-117 
## [1] "myfit_mdl: predict complete: 13.200000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                        feats
## 1 Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      4.248                 0.232
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1        0.602811    0.6001913    0.6054306       0.3516187
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.65       0.6610908        0.5891041
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5479974             0.5773698     0.1762674
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5741893    0.5608365    0.5875421       0.4088125
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.75          0.6475        0.4964286
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4667425             0.5261334    0.04601965
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01205845      0.02559737
## [1] "myfit_mdl: exit: 13.215000 secs"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 286.677 299.897
## 4 fit.models_1_All.X          1          3         glm 299.898      NA
##   elapsed
## 3  13.221
## 4      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] "    indepVar: Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.723000 secs"
## + Fold1.Rep1: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 3.389000 secs"

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4759  -1.1265   0.6646   1.1334   1.7794  
## 
## Coefficients: (5 not defined because of singularities)
##                                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                       0.37919    0.25366   1.495  0.13494    
## .rnorm                           -0.02215    0.03096  -0.715  0.47431    
## Edn.fctr.L                        0.10279    0.13497   0.762  0.44631    
## Edn.fctr.Q                        0.12567    0.12888   0.975  0.32954    
## Edn.fctr.C                        0.04192    0.11624   0.361  0.71836    
## `Edn.fctr^4`                     -0.29425    0.11456  -2.569  0.01021 *  
## `Edn.fctr^5`                     -0.11503    0.10553  -1.090  0.27572    
## `Edn.fctr^6`                      0.07371    0.09524   0.774  0.43899    
## `Edn.fctr^7`                      0.26530    0.10668   2.487  0.01288 *  
## Gender.fctrF                     -0.17773    0.22355  -0.795  0.42658    
## Gender.fctrM                     -0.48118    0.22081  -2.179  0.02932 *  
## Hhold.fctrMKn                    -0.06732    0.20701  -0.325  0.74502    
## Hhold.fctrMKy                    -0.25991    0.17902  -1.452  0.14654    
## Hhold.fctrPKn                     0.69878    0.37502   1.863  0.06242 .  
## Hhold.fctrPKy                     0.44360    0.49706   0.892  0.37215    
## Hhold.fctrSKn                     0.08695    0.16635   0.523  0.60120    
## Hhold.fctrSKy                     0.13728    0.29716   0.462  0.64412    
## Income.fctr.L                    -0.04282    0.09487  -0.451  0.65176    
## Income.fctr.Q                    -0.13497    0.08888  -1.519  0.12886    
## Income.fctr.C                    -0.19025    0.08846  -2.151  0.03150 *  
## `Income.fctr^4`                  -0.05189    0.08625  -0.602  0.54739    
## `Income.fctr^5`                   0.01610    0.08798   0.183  0.85477    
## `Income.fctr^6`                   0.04691    0.08553   0.548  0.58341    
## YOB.Age.fctr.L                    0.39661    0.15884   2.497  0.01253 *  
## YOB.Age.fctr.Q                    0.20719    0.13101   1.582  0.11376    
## YOB.Age.fctr.C                   -0.02754    0.11903  -0.231  0.81703    
## `YOB.Age.fctr^4`                  0.18537    0.11604   1.597  0.11016    
## `YOB.Age.fctr^5`                  0.10257    0.10782   0.951  0.34147    
## `YOB.Age.fctr^6`                  0.14949    0.09684   1.544  0.12266    
## `YOB.Age.fctr^7`                 -0.15679    0.09261  -1.693  0.09046 .  
## `YOB.Age.fctr^8`                 -0.18567    0.09559  -1.942  0.05208 .  
## `Hhold.fctrN:.clusterid.fctr2`   -0.03154    0.26765  -0.118  0.90620    
## `Hhold.fctrMKn:.clusterid.fctr2`  1.27461    0.26803   4.755 1.98e-06 ***
## `Hhold.fctrMKy:.clusterid.fctr2` -0.40854    0.14363  -2.844  0.00445 ** 
## `Hhold.fctrPKn:.clusterid.fctr2`  1.82293    0.69047   2.640  0.00829 ** 
## `Hhold.fctrPKy:.clusterid.fctr2` -0.68158    0.68188  -1.000  0.31752    
## `Hhold.fctrSKn:.clusterid.fctr2`  0.01341    0.10588   0.127  0.89924    
## `Hhold.fctrSKy:.clusterid.fctr2`  0.15773    0.41516   0.380  0.70401    
## `Hhold.fctrN:.clusterid.fctr3`    0.40431    0.27574   1.466  0.14257    
## `Hhold.fctrMKn:.clusterid.fctr3` -0.64499    0.25281  -2.551  0.01073 *  
## `Hhold.fctrMKy:.clusterid.fctr3`  0.90618    0.19851   4.565 4.99e-06 ***
## `Hhold.fctrPKn:.clusterid.fctr3`  0.01012    0.52155   0.019  0.98452    
## `Hhold.fctrPKy:.clusterid.fctr3`  0.52620    0.74624   0.705  0.48073    
## `Hhold.fctrSKn:.clusterid.fctr3`  0.90165    0.13065   6.901 5.16e-12 ***
## `Hhold.fctrSKy:.clusterid.fctr3`  0.98349    0.45447   2.164  0.03046 *  
## `Hhold.fctrN:.clusterid.fctr4`         NA         NA      NA       NA    
## `Hhold.fctrMKn:.clusterid.fctr4` -0.43725    0.30407  -1.438  0.15045    
## `Hhold.fctrMKy:.clusterid.fctr4`       NA         NA      NA       NA    
## `Hhold.fctrPKn:.clusterid.fctr4`  0.47249    0.51468   0.918  0.35861    
## `Hhold.fctrPKy:.clusterid.fctr4`       NA         NA      NA       NA    
## `Hhold.fctrSKn:.clusterid.fctr4`       NA         NA      NA       NA    
## `Hhold.fctrSKy:.clusterid.fctr4`       NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6150.3  on 4447  degrees of freedom
## Residual deviance: 5814.4  on 4401  degrees of freedom
## AIC: 5908.4
## 
## Number of Fisher Scoring iterations: 5
## 
## [1] "myfit_mdl: train diagnostics complete: 4.683000 secs"

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##          Prediction
## Reference    R    D
##         R 1949  142
##         D 1847  510
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.528327e-01   1.419360e-01   5.380780e-01   5.675180e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   1.135823e-03  2.088761e-319

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##          Prediction
## Reference   R   D
##         R 499  27
##         D 525  69
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.071429e-01   6.149811e-02   4.774308e-01   5.368173e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.436152e-01   2.549910e-99 
## [1] "myfit_mdl: predict complete: 12.034000 secs"
##               id
## 1 All.X##rcv#glm
##                                                                                        feats
## 1 Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                      2.646                 0.155
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6084494    0.6174079    0.5994909       0.3486086
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.7       0.6621369         0.586406
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1              0.538078              0.567518     0.1716844
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5711199    0.5513308    0.5909091       0.4091645
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.75        0.643871        0.5071429
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4774308             0.5368173    0.06149811
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.008938476      0.01945887
## [1] "myfit_mdl: exit: 12.055000 secs"

# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
                label.minor = "preProc")

##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 299.898 312.004
## 5 fit.models_1_preProc          1          4     preProc 312.005      NA
##   elapsed
## 4  12.107
## 5      NA

require(gdata)

## Loading required package: gdata

## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.

## 

## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.

## 
## Attaching package: 'gdata'

## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last

## The following object is masked from 'package:stats':
## 
##     nobs

## The following object is masked from 'package:utils':
## 
##     object.size

mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
                                                      "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    
} else fitobs_df <- glbObsFit

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glbMdlTuneParams,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indepVar=indepVar, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glbObsOOB)
}            
    
    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indepVar
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
#           cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
#           mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indepVar_lst <- list()
#     for (feat in setdiff(names(glbObsFit), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indepVar_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indepVar_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indepVar=indepVar,
#                             rsp_var=glb_rsp_var,
#                             fit_df=glbObsFit, OOB_df=glbObsOOB,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glbObsFit, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)

##                                                    id
## MFO###myMFO_classfr               MFO###myMFO_classfr
## Random###myrandom_classfr   Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart             Max.cor.Y##rcv#rpart
## Low.cor.X##rcv#glmnet           Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                   All.X##rcv#glmnet
## All.X##rcv#glm                         All.X##rcv#glm
##                                                                                                                 feats
## MFO###myMFO_classfr                                                                                            .rnorm
## Random###myrandom_classfr                                                                                      .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                Gender.fctr,.clusterid.fctr
## Max.cor.Y##rcv#rpart                                                                      Gender.fctr,.clusterid.fctr
## Low.cor.X##rcv#glmnet      Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glmnet          Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glm             Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##                            max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr                      0                      0.523
## Random###myrandom_classfr                0                      0.274
## Max.cor.Y.rcv.1X1###glmnet               0                      0.799
## Max.cor.Y##rcv#rpart                     5                      1.611
## Low.cor.X##rcv#glmnet                   25                      4.133
## All.X##rcv#glmnet                       25                      4.248
## All.X##rcv#glm                           1                      2.646
##                            min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr                        0.003       0.5000000
## Random###myrandom_classfr                  0.003       0.4942483
## Max.cor.Y.rcv.1X1###glmnet                 0.059       0.5701775
## Max.cor.Y##rcv#rpart                       0.018       0.5701775
## Low.cor.X##rcv#glmnet                      0.231       0.6028110
## All.X##rcv#glmnet                          0.232       0.6028110
## All.X##rcv#glm                             0.155       0.6084494
##                            max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr           0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr     0.4619799    0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet    0.5820182    0.5583369       0.4116226
## Max.cor.Y##rcv#rpart          0.5820182    0.5583369       0.4173716
## Low.cor.X##rcv#glmnet         0.6001913    0.6054306       0.3516187
## All.X##rcv#glmnet             0.6001913    0.6054306       0.3516187
## All.X##rcv#glm                0.6174079    0.5994909       0.3486086
##                            opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                          0.50       0.6395473
## Random###myrandom_classfr                    0.55       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                   0.70       0.6424599
## Max.cor.Y##rcv#rpart                         0.70       0.6395473
## Low.cor.X##rcv#glmnet                        0.65       0.6610908
## All.X##rcv#glmnet                            0.65       0.6610908
## All.X##rcv#glm                               0.70       0.6621369
##                            max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr               0.4700989             0.4553427
## Random###myrandom_classfr         0.4700989             0.4553427
## Max.cor.Y.rcv.1X1###glmnet        0.5085432             0.4937375
## Max.cor.Y##rcv#rpart              0.5694703             0.4553427
## Low.cor.X##rcv#glmnet             0.5891041             0.5479974
## All.X##rcv#glmnet                 0.5891041             0.5479974
## All.X##rcv#glm                    0.5864060             0.5380780
##                            max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr                    0.4848945    0.00000000
## Random###myrandom_classfr              0.4848945    0.00000000
## Max.cor.Y.rcv.1X1###glmnet             0.5233376    0.06243999
## Max.cor.Y##rcv#rpart                   0.4848945    0.13973417
## Low.cor.X##rcv#glmnet                  0.5773698    0.17626735
## All.X##rcv#glmnet                      0.5773698    0.17626735
## All.X##rcv#glm                         0.5675180    0.17168444
##                            max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr              0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr        0.5235690    0.5000000    0.5471380
## Max.cor.Y.rcv.1X1###glmnet       0.5575719    0.5646388    0.5505051
## Max.cor.Y##rcv#rpart             0.5575719    0.5646388    0.5505051
## Low.cor.X##rcv#glmnet            0.5741893    0.5608365    0.5875421
## All.X##rcv#glmnet                0.5741893    0.5608365    0.5875421
## All.X##rcv#glm                   0.5711199    0.5513308    0.5909091
##                            max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr              0.5000000                   0.50
## Random###myrandom_classfr        0.5191202                   0.55
## Max.cor.Y.rcv.1X1###glmnet       0.4278671                   0.70
## Max.cor.Y##rcv#rpart             0.4362830                   0.70
## Low.cor.X##rcv#glmnet            0.4088125                   0.75
## All.X##rcv#glmnet                0.4088125                   0.75
## All.X##rcv#glm                   0.4091645                   0.75
##                            max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr              0.6391252        0.4696429
## Random###myrandom_classfr        0.6391252        0.4696429
## Max.cor.Y.rcv.1X1###glmnet       0.6497065        0.5205357
## Max.cor.Y##rcv#rpart             0.6391252        0.4696429
## Low.cor.X##rcv#glmnet            0.6475000        0.4964286
## All.X##rcv#glmnet                0.6475000        0.4964286
## All.X##rcv#glm                   0.6438710        0.5071429
##                            max.AccuracyLower.OOB max.AccuracyUpper.OOB
## MFO###myMFO_classfr                    0.4400805             0.4993651
## Random###myrandom_classfr              0.4400805             0.4993651
## Max.cor.Y.rcv.1X1###glmnet             0.4908101             0.5501532
## Max.cor.Y##rcv#rpart                   0.4400805             0.4993651
## Low.cor.X##rcv#glmnet                  0.4667425             0.5261334
## All.X##rcv#glmnet                      0.4667425             0.5261334
## All.X##rcv#glm                         0.4774308             0.5368173
##                            max.Kappa.OOB max.AccuracySD.fit
## MFO###myMFO_classfr           0.00000000                 NA
## Random###myrandom_classfr     0.00000000                 NA
## Max.cor.Y.rcv.1X1###glmnet    0.08539590                 NA
## Max.cor.Y##rcv#rpart          0.00000000        0.010611699
## Low.cor.X##rcv#glmnet         0.04601965        0.012058450
## All.X##rcv#glmnet             0.04601965        0.012058450
## All.X##rcv#glm                0.06149811        0.008938476
##                            max.KappaSD.fit
## MFO###myMFO_classfr                     NA
## Random###myrandom_classfr               NA
## Max.cor.Y.rcv.1X1###glmnet              NA
## Max.cor.Y##rcv#rpart            0.02100317
## Low.cor.X##rcv#glmnet           0.02559737
## All.X##rcv#glmnet               0.02559737
## All.X##rcv#glm                  0.01945887

rm(ret_lst)
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
                label.minor = "teardown")

##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 312.005 313.504
## 6     fit.models_1_end          1          5    teardown 313.505      NA
##   elapsed
## 5   1.499
## 6      NA

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)

##        label step_major step_minor label_minor     bgn     end elapsed
## 5 fit.models          4          1           1 283.125 313.514  30.389
## 6 fit.models          4          2           2 313.514      NA      NA

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 317.527  NA      NA

plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)

##                                                    id
## MFO###myMFO_classfr               MFO###myMFO_classfr
## Random###myrandom_classfr   Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart             Max.cor.Y##rcv#rpart
## Low.cor.X##rcv#glmnet           Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                   All.X##rcv#glmnet
## All.X##rcv#glm                         All.X##rcv#glm
##                                                                                                                 feats
## MFO###myMFO_classfr                                                                                            .rnorm
## Random###myrandom_classfr                                                                                      .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                Gender.fctr,.clusterid.fctr
## Max.cor.Y##rcv#rpart                                                                      Gender.fctr,.clusterid.fctr
## Low.cor.X##rcv#glmnet      Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glmnet          Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glm             Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##                            max.nTuningRuns max.AUCpROC.fit max.Sens.fit
## MFO###myMFO_classfr                      0       0.5000000    0.0000000
## Random###myrandom_classfr                0       0.4942483    0.4619799
## Max.cor.Y.rcv.1X1###glmnet               0       0.5701775    0.5820182
## Max.cor.Y##rcv#rpart                     5       0.5701775    0.5820182
## Low.cor.X##rcv#glmnet                   25       0.6028110    0.6001913
## All.X##rcv#glmnet                       25       0.6028110    0.6001913
## All.X##rcv#glm                           1       0.6084494    0.6174079
##                            max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr           1.0000000       0.5000000
## Random###myrandom_classfr     0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet    0.5583369       0.4116226
## Max.cor.Y##rcv#rpart          0.5583369       0.4173716
## Low.cor.X##rcv#glmnet         0.6054306       0.3516187
## All.X##rcv#glmnet             0.6054306       0.3516187
## All.X##rcv#glm                0.5994909       0.3486086
##                            opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                          0.50       0.6395473
## Random###myrandom_classfr                    0.55       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                   0.70       0.6424599
## Max.cor.Y##rcv#rpart                         0.70       0.6395473
## Low.cor.X##rcv#glmnet                        0.65       0.6610908
## All.X##rcv#glmnet                            0.65       0.6610908
## All.X##rcv#glm                               0.70       0.6621369
##                            max.Accuracy.fit max.Kappa.fit max.AUCpROC.OOB
## MFO###myMFO_classfr               0.4700989    0.00000000       0.5000000
## Random###myrandom_classfr         0.4700989    0.00000000       0.5235690
## Max.cor.Y.rcv.1X1###glmnet        0.5085432    0.06243999       0.5575719
## Max.cor.Y##rcv#rpart              0.5694703    0.13973417       0.5575719
## Low.cor.X##rcv#glmnet             0.5891041    0.17626735       0.5741893
## All.X##rcv#glmnet                 0.5891041    0.17626735       0.5741893
## All.X##rcv#glm                    0.5864060    0.17168444       0.5711199
##                            max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## MFO###myMFO_classfr           0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr     0.5000000    0.5471380       0.5191202
## Max.cor.Y.rcv.1X1###glmnet    0.5646388    0.5505051       0.4278671
## Max.cor.Y##rcv#rpart          0.5646388    0.5505051       0.4362830
## Low.cor.X##rcv#glmnet         0.5608365    0.5875421       0.4088125
## All.X##rcv#glmnet             0.5608365    0.5875421       0.4088125
## All.X##rcv#glm                0.5513308    0.5909091       0.4091645
##                            opt.prob.threshold.OOB max.f.score.OOB
## MFO###myMFO_classfr                          0.50       0.6391252
## Random###myrandom_classfr                    0.55       0.6391252
## Max.cor.Y.rcv.1X1###glmnet                   0.70       0.6497065
## Max.cor.Y##rcv#rpart                         0.70       0.6391252
## Low.cor.X##rcv#glmnet                        0.75       0.6475000
## All.X##rcv#glmnet                            0.75       0.6475000
## All.X##rcv#glm                               0.75       0.6438710
##                            max.Accuracy.OOB max.Kappa.OOB
## MFO###myMFO_classfr               0.4696429    0.00000000
## Random###myrandom_classfr         0.4696429    0.00000000
## Max.cor.Y.rcv.1X1###glmnet        0.5205357    0.08539590
## Max.cor.Y##rcv#rpart              0.4696429    0.00000000
## Low.cor.X##rcv#glmnet             0.4964286    0.04601965
## All.X##rcv#glmnet                 0.4964286    0.04601965
## All.X##rcv#glm                    0.5071429    0.06149811
##                            inv.elapsedtime.everything
## MFO###myMFO_classfr                         1.9120459
## Random###myrandom_classfr                   3.6496350
## Max.cor.Y.rcv.1X1###glmnet                  1.2515645
## Max.cor.Y##rcv#rpart                        0.6207325
## Low.cor.X##rcv#glmnet                       0.2419550
## All.X##rcv#glmnet                           0.2354049
## All.X##rcv#glm                              0.3779289
##                            inv.elapsedtime.final
## MFO###myMFO_classfr                   333.333333
## Random###myrandom_classfr             333.333333
## Max.cor.Y.rcv.1X1###glmnet             16.949153
## Max.cor.Y##rcv#rpart                   55.555556
## Low.cor.X##rcv#glmnet                   4.329004
## All.X##rcv#glmnet                       4.310345
## All.X##rcv#glm                          6.451613

# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}

## Warning: max.AccuracyUpper.fit already exists in glb_models_df

## [1] "var:max.KappaSD.fit"

# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))

## Warning: Removed 4 rows containing missing values (geom_errorbar).

dev.off()

## quartz_off_screen 
##                 2

print(gp)

## Warning: Removed 4 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])

##                           id max.Accuracy.OOB max.AUCROCR.OOB
## 3 Max.cor.Y.rcv.1X1###glmnet        0.5205357       0.4278671
## 7             All.X##rcv#glm        0.5071429       0.4091645
## 5      Low.cor.X##rcv#glmnet        0.4964286       0.4088125
## 6          All.X##rcv#glmnet        0.4964286       0.4088125
## 2  Random###myrandom_classfr        0.4696429       0.5191202
## 1        MFO###myMFO_classfr        0.4696429       0.5000000
## 4       Max.cor.Y##rcv#rpart        0.4696429       0.4362830
##   max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 3       0.5575719        0.5085432                   0.70
## 7       0.5711199        0.5864060                   0.70
## 5       0.5741893        0.5891041                   0.65
## 6       0.5741893        0.5891041                   0.65
## 2       0.5235690        0.4700989                   0.55
## 1       0.5000000        0.4700989                   0.50
## 4       0.5575719        0.5694703                   0.70
##   opt.prob.threshold.OOB
## 3                   0.70
## 7                   0.75
## 5                   0.75
## 6                   0.75
## 2                   0.55
## 1                   0.50
## 4                   0.70

# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())

## [1] "Metrics used for model selection:"

## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7f95d6dc0078>

print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))

## [1] "Best model id: Max.cor.Y.rcv.1X1###glmnet"

glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indepVar <- paste(indepVar, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indepVar <- intersect(indepVar, names(glbObsFit))
    
#     indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
#     if (glb_is_classification && glb_is_binomial)
#         indepVar <- grep("prob$", indepVar, value=TRUE) else
#         indepVar <- indepVar[!grepl("err$", indepVar)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glbMdlSelId)) 
    glbMdlSelId <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glbMdlSelId))   

## [1] "User specified selection: All.X##rcv#glmnet"

myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])

##             Length Class      Mode     
## a0            58   -none-     numeric  
## beta        2958   dgCMatrix  S4       
## df            58   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        58   -none-     numeric  
## dev.ratio     58   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        51   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                    (Intercept)                         .rnorm 
##                   0.2191930523                  -0.0001578696 
##                     Edn.fctr.L                     Edn.fctr^4 
##                   0.0227319937                  -0.1825119570 
##                     Edn.fctr^5                     Edn.fctr^7 
##                  -0.0313213205                   0.1472718213 
##                   Gender.fctrM                  Hhold.fctrMKn 
##                  -0.2768838131                  -0.0033414146 
##                  Hhold.fctrMKy                  Hhold.fctrPKn 
##                  -0.2218131392                   0.6449818135 
##                  Hhold.fctrSKy                  Income.fctr.Q 
##                   0.0939556740                  -0.0647543426 
##                  Income.fctr.C                 YOB.Age.fctr.L 
##                  -0.1298650683                   0.2117746741 
##                 YOB.Age.fctr.Q                 YOB.Age.fctr^4 
##                   0.0649495145                   0.0877950477 
##                 YOB.Age.fctr^6                 YOB.Age.fctr^7 
##                   0.0694562859                  -0.0767162362 
##                 YOB.Age.fctr^8 Hhold.fctrMKn:.clusterid.fctr2 
##                  -0.1071714987                   1.0204049056 
## Hhold.fctrMKy:.clusterid.fctr2 Hhold.fctrPKn:.clusterid.fctr2 
##                  -0.3737679058                   1.2430889164 
## Hhold.fctrPKy:.clusterid.fctr2   Hhold.fctrN:.clusterid.fctr3 
##                  -0.0346131753                   0.1874531773 
## Hhold.fctrMKn:.clusterid.fctr3 Hhold.fctrMKy:.clusterid.fctr3 
##                  -0.5640516064                   0.7409635659 
## Hhold.fctrPKy:.clusterid.fctr3 Hhold.fctrSKn:.clusterid.fctr3 
##                   0.5582357714                   0.8358309089 
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr4 
##                   0.7109593830                  -0.3408243730 
## Hhold.fctrPKn:.clusterid.fctr4 
##                   0.1620282232 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                         .rnorm 
##                    0.224848967                   -0.002034718 
##                     Edn.fctr.L                     Edn.fctr^4 
##                    0.022961291                   -0.194514138 
##                     Edn.fctr^5                     Edn.fctr^7 
##                   -0.036840793                    0.155379148 
##                   Gender.fctrM                  Hhold.fctrMKn 
##                   -0.279933696                   -0.016301531 
##                  Hhold.fctrMKy                  Hhold.fctrPKn 
##                   -0.231905525                    0.642479758 
##                  Hhold.fctrSKy                  Income.fctr.Q 
##                    0.095893072                   -0.070064887 
##                  Income.fctr.C                 YOB.Age.fctr.L 
##                   -0.135092466                    0.230026983 
##                 YOB.Age.fctr.Q                 YOB.Age.fctr^4 
##                    0.076434784                    0.098777478 
##                 YOB.Age.fctr^5                 YOB.Age.fctr^6 
##                    0.002459613                    0.077008267 
##                 YOB.Age.fctr^7                 YOB.Age.fctr^8 
##                   -0.082901882                   -0.113404386 
## Hhold.fctrMKn:.clusterid.fctr2 Hhold.fctrMKy:.clusterid.fctr2 
##                    1.040824952                   -0.377065873 
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrPKy:.clusterid.fctr2 
##                    1.283976001                   -0.061655569 
##   Hhold.fctrN:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr3 
##                    0.201531092                   -0.571036695 
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr3 
##                    0.754435537                    0.583943428 
## Hhold.fctrSKn:.clusterid.fctr3 Hhold.fctrSKy:.clusterid.fctr3 
##                    0.841193304                    0.727138575 
## Hhold.fctrMKn:.clusterid.fctr4 Hhold.fctrPKn:.clusterid.fctr4 
##                   -0.349784240                    0.186310773

## [1] TRUE

# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))

## [1] "All.X##rcv#glmnet fit prediction diagnostics:"

glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))

## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"

glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))

##                                All.X..rcv.glmnet.imp          imp
## Hhold.fctrPKn:.clusterid.fctr2          100.00000000 100.00000000
## Hhold.fctrMKn:.clusterid.fctr2           81.77328327  81.77328327
## Hhold.fctrSKn:.clusterid.fctr3           66.71127815  66.71127815
## Hhold.fctrMKy:.clusterid.fctr3           59.34710406  59.34710406
## Hhold.fctrSKy:.clusterid.fctr3           57.02138879  57.02138879
## Hhold.fctrPKn                            51.32067394  51.32067394
## Hhold.fctrMKn:.clusterid.fctr3           45.09955647  45.09955647
## Hhold.fctrPKy:.clusterid.fctr3           45.08208113  45.08208113
## Hhold.fctrMKy:.clusterid.fctr2           29.85346336  29.85346336
## Hhold.fctrMKn:.clusterid.fctr4           27.36395190  27.36395190
## Gender.fctrM                             22.12961864  22.12961864
## Hhold.fctrMKy                            17.91029979  17.91029979
## YOB.Age.fctr.L                           17.30492474  17.30492474
## Hhold.fctrN:.clusterid.fctr3             15.26803555  15.26803555
## Edn.fctr^4                               14.82498277  14.82498277
## Hhold.fctrPKn:.clusterid.fctr4           13.48563771  13.48563771
## Edn.fctr^7                               11.92495425  11.92495425
## Income.fctr.C                            10.46972821  10.46972821
## YOB.Age.fctr^8                            8.68586599   8.68586599
## Hhold.fctrSKy                             7.53078837   7.53078837
## YOB.Age.fctr^4                            7.25540427   7.25540427
## YOB.Age.fctr^7                            6.25862757   6.25862757
## YOB.Age.fctr^6                            5.71282392   5.71282392
## YOB.Age.fctr.Q                            5.44746725   5.44746725
## Income.fctr.Q                             5.28488657   5.28488657
## Hhold.fctrPKy:.clusterid.fctr2            3.40127469   3.40127469
## Edn.fctr^5                                2.62653511   2.62653511
## Edn.fctr.L                                1.81632597   1.81632597
## Hhold.fctrMKn                             0.57478947   0.57478947
## YOB.Age.fctr^5                            0.05856849   0.05856849
## .rnorm                                    0.05726777   0.05726777
## Edn.fctr.Q                                0.00000000   0.00000000
## Edn.fctr.C                                0.00000000   0.00000000
## Edn.fctr^6                                0.00000000   0.00000000
## Gender.fctrF                              0.00000000   0.00000000
## Hhold.fctrPKy                             0.00000000   0.00000000
## Hhold.fctrSKn                             0.00000000   0.00000000
## Income.fctr.L                             0.00000000   0.00000000
## Income.fctr^4                             0.00000000   0.00000000
## Income.fctr^5                             0.00000000   0.00000000
## Income.fctr^6                             0.00000000   0.00000000
## YOB.Age.fctr.C                            0.00000000   0.00000000
## Hhold.fctrN:.clusterid.fctr2              0.00000000   0.00000000
## Hhold.fctrSKn:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrSKy:.clusterid.fctr2            0.00000000   0.00000000
## Hhold.fctrPKn:.clusterid.fctr3            0.00000000   0.00000000
## Hhold.fctrN:.clusterid.fctr4              0.00000000   0.00000000
## Hhold.fctrMKy:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrPKy:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrSKn:.clusterid.fctr4            0.00000000   0.00000000
## Hhold.fctrSKy:.clusterid.fctr4            0.00000000   0.00000000

#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        require(lazyeval)
        
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId, 
            prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId)                  

## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 7

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    4572          D                         0.2937301
## 2    2466          D                         0.3069803
## 3    2798          D                         0.3146344
## 4    5591          D                         0.3156183
## 5    5517          D                         0.3170324
## 6    4862          D                         0.3180935
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            R                             TRUE
## 2                            R                             TRUE
## 3                            R                             TRUE
## 4                            R                             TRUE
## 5                            R                             TRUE
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.7062699                               FALSE
## 2                            0.6930197                               FALSE
## 3                            0.6853656                               FALSE
## 4                            0.6843817                               FALSE
## 5                            0.6829676                               FALSE
## 6                            0.6819065                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                         -0.4562699
## 2                                 FALSE                         -0.4430197
## 3                                 FALSE                         -0.4353656
## 4                                 FALSE                         -0.4343817
## 5                                 FALSE                         -0.4329676
## 6                                 FALSE                         -0.4319065
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 14     5028          D                         0.3605508
## 119    2097          D                         0.4631947
## 232    1341          D                         0.4918281
## 283    4299          D                         0.5126812
## 347     901          D                         0.5401236
## 507     355          D                         0.7036293
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 14                             R                             TRUE
## 119                            R                             TRUE
## 232                            R                             TRUE
## 283                            R                             TRUE
## 347                            R                             TRUE
## 507                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 14                             0.6394492
## 119                            0.5368053
## 232                            0.5081719
## 283                            0.4873188
## 347                            0.4598764
## 507                            0.2963707
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 14                                FALSE
## 119                               FALSE
## 232                               FALSE
## 283                               FALSE
## 347                               FALSE
## 507                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 14                                  FALSE
## 119                                 FALSE
## 232                                 FALSE
## 283                                 FALSE
## 347                                 FALSE
## 507                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 14                         -0.38944924
## 119                        -0.28680535
## 232                        -0.25817187
## 283                        -0.23731877
## 347                        -0.20987642
## 507                        -0.04637073
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 559    2202          R                         0.7562489
## 560    5702          R                         0.7597542
## 561    5148          R                         0.7770209
## 562    1008          R                         0.7871790
## 563    4848          R                         0.7907140
## 564    5088          R                         0.8685052
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 559                            D                             TRUE
## 560                            D                             TRUE
## 561                            D                             TRUE
## 562                            D                             TRUE
## 563                            D                             TRUE
## 564                            D                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 559                            0.7562489
## 560                            0.7597542
## 561                            0.7770209
## 562                            0.7871790
## 563                            0.7907140
## 564                            0.8685052
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 559                               FALSE
## 560                               FALSE
## 561                               FALSE
## 562                               FALSE
## 563                               FALSE
## 564                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 559                                 FALSE
## 560                                 FALSE
## 561                                 FALSE
## 562                                 FALSE
## 563                                 FALSE
## 564                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 559                        0.006248905
## 560                        0.009754177
## 561                        0.027020899
## 562                        0.037179036
## 563                        0.040713986
## 564                        0.118505209

if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}

##     Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## N            N     83    367    102     0.08250899    0.074107143
## PKy        PKy      9     52     10     0.01169065    0.008035714
## PKn        PKn     30    150     37     0.03372302    0.026785714
## SKn        SKn    511   1920    638     0.43165468    0.456250000
## MKy        MKy    298   1296    371     0.29136691    0.266071429
## SKy        SKy     53    147     65     0.03304856    0.047321429
## MKn        MKn    136    516    169     0.11600719    0.121428571
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## N      0.073275862       181.29252        0.4939851    367        41.33093
## PKy    0.007183908        24.24401        0.4662309     52         4.47857
## PKn    0.026580460        52.79457        0.3519638    150        14.59385
## SKn    0.458333333       906.92866        0.4723587   1920       246.49425
## MKy    0.266522989       618.48343        0.4772249   1296       143.69229
## SKy    0.046695402        66.26483        0.4507811    147        24.93247
## MKn    0.121408046       235.04678        0.4555170    516        63.48079
##     err.abs.OOB.mean
## N          0.4979630
## PKy        0.4976189
## PKn        0.4864616
## SKn        0.4823762
## MKy        0.4821889
## SKy        0.4704240
## MKn        0.4667705
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##      1120.000000      4448.000000      1392.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000      2085.054783         3.168061 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      4448.000000       539.003152         3.383803

write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 326.008  NA      NA

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)

##        label step_major step_minor label_minor     bgn     end elapsed
## 6 fit.models          4          2           2 313.514 326.018  12.504
## 7 fit.models          4          3           3 326.019      NA      NA

# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))

## character(0)

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)

##               label step_major step_minor label_minor     bgn    end
## 7        fit.models          4          3           3 326.019 330.76
## 8 fit.data.training          5          0           0 330.761     NA
##   elapsed
## 7   4.741
## 8      NA

Step 5.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{    
    warning("Final model same as glbMdlSelId")
    glbMdlFinId <- paste0("Final.", glbMdlSelId)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
    mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
    mdlDf$id <- glbMdlFinId
    glb_models_df <- rbind(glb_models_df, mdlDf)
} else {    
            if (grepl("RFE\\.X", names(glbMdlFamilies))) {
                indepVar <- mygetIndepVar(glb_feats_df)
                rfe_trn_results <- 
                    myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
                if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                                      sort(predictors(rfe_fit_results))))) {
                    print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
                    print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
                    print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
                    print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
            }
        }
    # }    

    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indepVar = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
        
    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
        indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
    } else indepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glbMdlSelId
                                                   , "feats"], "[,]")))
        
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glbMdlSelId)) != -1))
        ths_preProcess <- str_sub(glbMdlSelId, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
                                   "Final.Ensemble", "Final")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indepVar) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = ths_preProcess)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
        
        if ((length(method_vctr) == 1) || (method != "glm")) {
            glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
            glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
        }
    }
        
}

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] "    indepVar: Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.695000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0254 on full training set
## [1] "myfit_mdl: train complete: 4.985000 secs"

## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0            53   -none-     numeric  
## beta        2703   dgCMatrix  S4       
## df            53   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        53   -none-     numeric  
## dev.ratio     53   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        51   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                    (Intercept)                     Edn.fctr.L 
##                    0.148237677                    0.086346255 
##                     Edn.fctr^4                     Edn.fctr^7 
##                   -0.002223362                    0.010378488 
##                   Gender.fctrF                   Gender.fctrM 
##                    0.096141649                   -0.193432151 
##                  Hhold.fctrMKy                  Hhold.fctrPKn 
##                   -0.103086930                    0.444491419 
##                  Income.fctr.Q                  Income.fctr.C 
##                   -0.026737738                   -0.054538464 
##                 YOB.Age.fctr.L                 YOB.Age.fctr^6 
##                    0.061953062                    0.005340885 
##                 YOB.Age.fctr^7                 YOB.Age.fctr^8 
##                   -0.014976899                   -0.053320720 
## Hhold.fctrMKn:.clusterid.fctr2 Hhold.fctrMKy:.clusterid.fctr2 
##                    0.792382775                   -0.396724951 
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrPKy:.clusterid.fctr2 
##                    0.889420794                   -0.014330398 
##   Hhold.fctrN:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr3 
##                    0.100478261                   -0.367912448 
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr3 
##                    0.536373210                    0.331328585 
## Hhold.fctrSKn:.clusterid.fctr3 Hhold.fctrSKy:.clusterid.fctr3 
##                    0.619945516                    0.579126545 
## Hhold.fctrMKn:.clusterid.fctr4 
##                   -0.239616388 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                     Edn.fctr.L 
##                   0.1512974026                   0.0881978898 
##                     Edn.fctr^4                     Edn.fctr^5 
##                  -0.0169169604                  -0.0005084800 
##                     Edn.fctr^7                   Gender.fctrF 
##                   0.0187522917                   0.0952586873 
##                   Gender.fctrM                  Hhold.fctrMKy 
##                  -0.1988710629                  -0.1127692813 
##                  Hhold.fctrPKn                  Income.fctr.Q 
##                   0.4574163268                  -0.0341476466 
##                  Income.fctr.C                 YOB.Age.fctr.L 
##                  -0.0639570675                   0.0822663701 
##                 YOB.Age.fctr^6                 YOB.Age.fctr^7 
##                   0.0163974842                  -0.0255520309 
##                 YOB.Age.fctr^8 Hhold.fctrMKn:.clusterid.fctr2 
##                  -0.0639441606                   0.8143319059 
## Hhold.fctrMKy:.clusterid.fctr2 Hhold.fctrPKn:.clusterid.fctr2 
##                  -0.4048403846                   0.9305434022 
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2 
##                  -0.0559218971                   0.0009573626 
##   Hhold.fctrN:.clusterid.fctr3 Hhold.fctrMKn:.clusterid.fctr3 
##                   0.1288526662                  -0.3917646906 
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr3 
##                   0.5604812046                   0.3814087293 
## Hhold.fctrSKn:.clusterid.fctr3 Hhold.fctrSKy:.clusterid.fctr3 
##                   0.6334473060                   0.6087872341 
## Hhold.fctrMKn:.clusterid.fctr4 
##                  -0.2700184317 
## [1] "myfit_mdl: train diagnostics complete: 5.602000 secs"

##          Prediction
## Reference    R    D
##         R 2448  169
##         D 2405  546
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5377155      0.1148891      0.5245131      0.5508784      0.5299928 
## AccuracyPValue  McnemarPValue 
##      0.1268807      0.0000000 
## [1] "myfit_mdl: predict complete: 8.354000 secs"
##                  id
## 1 Final##rcv#glmnet
##                                                                                        feats
## 1 Hhold.fctr,Edn.fctr,YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              20                      4.269                  0.22
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5943085    0.5976309    0.5909861       0.3674799
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.65       0.6554217        0.5891979
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5245131             0.5508784     0.1759431
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01395096      0.02759585
## [1] "myfit_mdl: exit: 8.370000 secs"

rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)

##               label step_major step_minor label_minor     bgn     end
## 8 fit.data.training          5          0           0 330.761 339.637
## 9 fit.data.training          5          1           1 339.638      NA
##   elapsed
## 8   8.877
## 9      NA

#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)

## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.75

glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)

##                                All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Hhold.fctrPKn:.clusterid.fctr2          100.00000000          1.000000e+02
## Hhold.fctrMKn:.clusterid.fctr2           81.77328327          8.886702e+01
## Hhold.fctrSKn:.clusterid.fctr3           66.71127815          6.947223e+01
## Hhold.fctrSKy:.clusterid.fctr3           57.02138879          6.515652e+01
## Hhold.fctrMKy:.clusterid.fctr3           59.34710406          6.029541e+01
## Hhold.fctrPKn                            51.32067394          4.985973e+01
## Hhold.fctrMKy:.clusterid.fctr2           29.85346336          4.444977e+01
## Hhold.fctrMKn:.clusterid.fctr3           45.09955647          4.146915e+01
## Hhold.fctrPKy:.clusterid.fctr3           45.08208113          3.777933e+01
## Hhold.fctrMKn:.clusterid.fctr4           27.36395190          2.723376e+01
## Gender.fctrM                             22.12961864          2.169496e+01
## Hhold.fctrMKy                            17.91029979          1.166490e+01
## Hhold.fctrN:.clusterid.fctr3             15.26803555          1.165690e+01
## Gender.fctrF                              0.00000000          1.072867e+01
## Edn.fctr.L                                1.81632597          9.675682e+00
## YOB.Age.fctr.L                           17.30492474          7.230168e+00
## Income.fctr.C                            10.46972821          6.236503e+00
## YOB.Age.fctr^8                            8.68586599          6.118714e+00
## Income.fctr.Q                             5.28488657          3.099822e+00
## Hhold.fctrPKy:.clusterid.fctr2            3.40127469          2.231902e+00
## YOB.Age.fctr^7                            6.25862757          1.833766e+00
## Edn.fctr^7                               11.92495425          1.286595e+00
## YOB.Age.fctr^6                            5.71282392          7.644211e-01
## Edn.fctr^4                               14.82498277          4.712513e-01
## Hhold.fctrSKn:.clusterid.fctr2            0.00000000          1.451861e-02
## Edn.fctr^5                                2.62653511          7.711211e-03
## .rnorm                                    0.05726777          0.000000e+00
## Edn.fctr.C                                0.00000000          0.000000e+00
## Edn.fctr.Q                                0.00000000          0.000000e+00
## Edn.fctr^6                                0.00000000          0.000000e+00
## Hhold.fctrMKn                             0.57478947          0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr4            0.00000000          0.000000e+00
## Hhold.fctrN:.clusterid.fctr2              0.00000000          0.000000e+00
## Hhold.fctrN:.clusterid.fctr4              0.00000000          0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr3            0.00000000          0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr4           13.48563771          0.000000e+00
## Hhold.fctrPKy                             0.00000000          0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr4            0.00000000          0.000000e+00
## Hhold.fctrSKn                             0.00000000          0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr4            0.00000000          0.000000e+00
## Hhold.fctrSKy                             7.53078837          0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr2            0.00000000          0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr4            0.00000000          0.000000e+00
## Income.fctr.L                             0.00000000          0.000000e+00
## Income.fctr^4                             0.00000000          0.000000e+00
## Income.fctr^5                             0.00000000          0.000000e+00
## Income.fctr^6                             0.00000000          0.000000e+00
## YOB.Age.fctr.C                            0.00000000          0.000000e+00
## YOB.Age.fctr.Q                            5.44746725          0.000000e+00
## YOB.Age.fctr^4                            7.25540427          0.000000e+00
## YOB.Age.fctr^5                            0.05856849          0.000000e+00
##                                         imp
## Hhold.fctrPKn:.clusterid.fctr2 1.000000e+02
## Hhold.fctrMKn:.clusterid.fctr2 8.886702e+01
## Hhold.fctrSKn:.clusterid.fctr3 6.947223e+01
## Hhold.fctrSKy:.clusterid.fctr3 6.515652e+01
## Hhold.fctrMKy:.clusterid.fctr3 6.029541e+01
## Hhold.fctrPKn                  4.985973e+01
## Hhold.fctrMKy:.clusterid.fctr2 4.444977e+01
## Hhold.fctrMKn:.clusterid.fctr3 4.146915e+01
## Hhold.fctrPKy:.clusterid.fctr3 3.777933e+01
## Hhold.fctrMKn:.clusterid.fctr4 2.723376e+01
## Gender.fctrM                   2.169496e+01
## Hhold.fctrMKy                  1.166490e+01
## Hhold.fctrN:.clusterid.fctr3   1.165690e+01
## Gender.fctrF                   1.072867e+01
## Edn.fctr.L                     9.675682e+00
## YOB.Age.fctr.L                 7.230168e+00
## Income.fctr.C                  6.236503e+00
## YOB.Age.fctr^8                 6.118714e+00
## Income.fctr.Q                  3.099822e+00
## Hhold.fctrPKy:.clusterid.fctr2 2.231902e+00
## YOB.Age.fctr^7                 1.833766e+00
## Edn.fctr^7                     1.286595e+00
## YOB.Age.fctr^6                 7.644211e-01
## Edn.fctr^4                     4.712513e-01
## Hhold.fctrSKn:.clusterid.fctr2 1.451861e-02
## Edn.fctr^5                     7.711211e-03
## .rnorm                         0.000000e+00
## Edn.fctr.C                     0.000000e+00
## Edn.fctr.Q                     0.000000e+00
## Edn.fctr^6                     0.000000e+00
## Hhold.fctrMKn                  0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr4 0.000000e+00
## Hhold.fctrN:.clusterid.fctr2   0.000000e+00
## Hhold.fctrN:.clusterid.fctr4   0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr3 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr4 0.000000e+00
## Hhold.fctrPKy                  0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr4 0.000000e+00
## Hhold.fctrSKn                  0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr4 0.000000e+00
## Hhold.fctrSKy                  0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr2 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr4 0.000000e+00
## Income.fctr.L                  0.000000e+00
## Income.fctr^4                  0.000000e+00
## Income.fctr^5                  0.000000e+00
## Income.fctr^6                  0.000000e+00
## YOB.Age.fctr.C                 0.000000e+00
## YOB.Age.fctr.Q                 0.000000e+00
## YOB.Age.fctr^4                 0.000000e+00
## YOB.Age.fctr^5                 0.000000e+00

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  

## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 7

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    2466          D                                NA
## 2    2967          D                         0.3070993
## 3    2798          D                                NA
## 4    3991          D                         0.3151989
## 5    2431          D                         0.3180465
## 6    3799          D                         0.3183448
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                         <NA>                               NA
## 2                            R                             TRUE
## 3                         <NA>                               NA
## 4                            R                             TRUE
## 5                            R                             TRUE
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                                   NA                                  NA
## 2                            0.6929007                               FALSE
## 3                                   NA                                  NA
## 4                            0.6848011                               FALSE
## 5                            0.6819535                               FALSE
## 6                            0.6816552                               FALSE
##   Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1                         0.3463593                            R
## 2                         0.3463593                            R
## 3                         0.3497938                            R
## 4                         0.3519284                            R
## 5                         0.3533973                            R
## 6                         0.3533973                            R
##   Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1                             TRUE                            0.6536407
## 2                             TRUE                            0.6536407
## 3                             TRUE                            0.6502062
## 4                             TRUE                            0.6480716
## 5                             TRUE                            0.6466027
## 6                             TRUE                            0.6466027
##   Party.fctr.Final..rcv.glmnet.is.acc
## 1                               FALSE
## 2                               FALSE
## 3                               FALSE
## 4                               FALSE
## 5                               FALSE
## 6                               FALSE
##   Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1                                 FALSE                         -0.4036407
## 2                                 FALSE                         -0.4036407
## 3                                 FALSE                         -0.4002062
## 4                                 FALSE                         -0.3980716
## 5                                 FALSE                         -0.3966027
## 6                                 FALSE                         -0.3966027
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 966     1228          D                         0.4862222
## 1266    1826          D                         0.5312718
## 1725    2249          D                                NA
## 2053    2974          D                                NA
## 2379    5464          D                         0.6863132
## 2742    1779          D                         0.7450169
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 966                             R                             TRUE
## 1266                            R                             TRUE
## 1725                         <NA>                               NA
## 2053                         <NA>                               NA
## 2379                            R                             TRUE
## 2742                            R                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 966                             0.5137778
## 1266                            0.4687282
## 1725                                   NA
## 2053                                   NA
## 2379                            0.3136868
## 2742                            0.2549831
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 966                                FALSE                         0.4859194
## 1266                               FALSE                         0.5061418
## 1725                                  NA                         0.5503917
## 2053                                  NA                         0.5722037
## 2379                               FALSE                         0.6431626
## 2742                               FALSE                         0.7026793
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 966                             R                             TRUE
## 1266                            R                             TRUE
## 1725                            R                             TRUE
## 2053                            R                             TRUE
## 2379                            R                             TRUE
## 2742                            R                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 966                             0.5140806
## 1266                            0.4938582
## 1725                            0.4496083
## 2053                            0.4277963
## 2379                            0.3568374
## 2742                            0.2973207
##      Party.fctr.Final..rcv.glmnet.is.acc
## 966                                FALSE
## 1266                               FALSE
## 1725                               FALSE
## 2053                               FALSE
## 2379                               FALSE
## 2742                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 966                                  FALSE
## 1266                                 FALSE
## 1725                                 FALSE
## 2053                                 FALSE
## 2379                                 FALSE
## 2742                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 966                          -0.2640806
## 1266                         -0.2438582
## 1725                         -0.1996083
## 2053                         -0.1777963
## 2379                         -0.1068374
## 2742                         -0.0473207
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2900    3413          R                         0.8110523
## 2901    3697          R                         0.8072371
## 2902    5088          R                                NA
## 2903    1236          R                         0.8789906
## 2904    2749          R                         0.8608611
## 2905    3895          R                         0.8969748
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2900                            D                             TRUE
## 2901                            D                             TRUE
## 2902                         <NA>                               NA
## 2903                            D                             TRUE
## 2904                            D                             TRUE
## 2905                            D                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 2900                            0.8110523
## 2901                            0.8072371
## 2902                                   NA
## 2903                            0.8789906
## 2904                            0.8608611
## 2905                            0.8969748
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2900                               FALSE                         0.7540231
## 2901                               FALSE                         0.7551430
## 2902                                  NA                         0.7867166
## 2903                               FALSE                         0.7942906
## 2904                               FALSE                         0.7945109
## 2905                               FALSE                         0.8404213
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2900                            D                             TRUE
## 2901                            D                             TRUE
## 2902                            D                             TRUE
## 2903                            D                             TRUE
## 2904                            D                             TRUE
## 2905                            D                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 2900                            0.7540231
## 2901                            0.7551430
## 2902                            0.7867166
## 2903                            0.7942906
## 2904                            0.7945109
## 2905                            0.8404213
##      Party.fctr.Final..rcv.glmnet.is.acc
## 2900                               FALSE
## 2901                               FALSE
## 2902                               FALSE
## 2903                               FALSE
## 2904                               FALSE
## 2905                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 2900                                 FALSE
## 2901                                 FALSE
## 2902                                 FALSE
## 2903                                 FALSE
## 2904                                 FALSE
## 2905                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 2900                        0.004023065
## 2901                        0.005143007
## 2902                        0.036716620
## 2903                        0.044290629
## 2904                        0.044510918
## 2905                        0.090421279

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))

## [1] "Party.fctr.Final..rcv.glmnet.prob"   
## [2] "Party.fctr.Final..rcv.glmnet"        
## [3] "Party.fctr.Final..rcv.glmnet.err"    
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"

for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))

## character(0)

print(setdiff(names(glbObsOOB), names(glbObsAll)))

## character(0)

for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))

## character(0)

#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.training.all.prediction 
## 2.0000    5   2 0 0 1

## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)

## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token

## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  model.final 
## 3.0000    4   2 0 1 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)

##                label step_major step_minor label_minor     bgn     end
## 9  fit.data.training          5          1           1 339.638 348.798
## 10  predict.data.new          6          0           0 348.799      NA
##    elapsed
## 9     9.16
## 10      NA

Step 6.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.75

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.75

## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 7

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## NULL

## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"

## [1] 0.75

## [1] "glbMdlSelId: All.X##rcv#glmnet"

## [1] "glbMdlFinId: Final##rcv#glmnet"

## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0

##                            max.Accuracy.OOB max.AUCROCR.OOB
## Max.cor.Y.rcv.1X1###glmnet        0.5205357       0.4278671
## All.X##rcv#glm                    0.5071429       0.4091645
## Low.cor.X##rcv#glmnet             0.4964286       0.4088125
## All.X##rcv#glmnet                 0.4964286       0.4088125
## Random###myrandom_classfr         0.4696429       0.5191202
## MFO###myMFO_classfr               0.4696429       0.5000000
## Max.cor.Y##rcv#rpart              0.4696429       0.4362830
## Final##rcv#glmnet                        NA              NA
##                            max.AUCpROC.OOB max.Accuracy.fit
## Max.cor.Y.rcv.1X1###glmnet       0.5575719        0.5085432
## All.X##rcv#glm                   0.5711199        0.5864060
## Low.cor.X##rcv#glmnet            0.5741893        0.5891041
## All.X##rcv#glmnet                0.5741893        0.5891041
## Random###myrandom_classfr        0.5235690        0.4700989
## MFO###myMFO_classfr              0.5000000        0.4700989
## Max.cor.Y##rcv#rpart             0.5575719        0.5694703
## Final##rcv#glmnet                       NA        0.5891979
##                            opt.prob.threshold.fit opt.prob.threshold.OOB
## Max.cor.Y.rcv.1X1###glmnet                   0.70                   0.70
## All.X##rcv#glm                               0.70                   0.75
## Low.cor.X##rcv#glmnet                        0.65                   0.75
## All.X##rcv#glmnet                            0.65                   0.75
## Random###myrandom_classfr                    0.55                   0.55
## MFO###myMFO_classfr                          0.50                   0.50
## Max.cor.Y##rcv#rpart                         0.70                   0.70
## Final##rcv#glmnet                            0.65                     NA

## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   R   D
##         R 518   8
##         D 556  38

##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## N         181.29252        41.33093       223.29770              NA
## PKy        24.24401         4.47857        29.54114              NA
## PKn        52.79457        14.59385        72.34852              NA
## SKn       906.92866       246.49425      1165.81817              NA
## MKy       618.48343       143.69229       769.99289              NA
## SKy        66.26483        24.93247        93.19849              NA
## MKn       235.04678        63.48079       304.80780              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## N       0.08250899    0.074107143    0.073275862    367       NA      102
## PKy     0.01169065    0.008035714    0.007183908     52       NA       10
## PKn     0.03372302    0.026785714    0.026580460    150        7       30
## SKn     0.43165468    0.456250000    0.458333333   1920       NA      638
## MKy     0.29136691    0.266071429    0.266522989   1296       NA      371
## SKy     0.03304856    0.047321429    0.046695402    147       NA       65
## MKn     0.11600719    0.121428571    0.121408046    516        2      167
##     .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## N       83      230      220    102    367    102    450        0.4979630
## PKy      9       35       26     10     52     10     61        0.4976189
## PKn     30      131       49     37    150     37    180        0.4864616
## SKn    511     1340     1091    638   1920    638   2431        0.4823762
## MKy    298      752      842    371   1296    371   1594        0.4821889
## SKy     53      119       81     65    147     65    200        0.4704240
## MKn    136      344      308    169    516    169    652        0.4667705
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## N          0.4939851               NA        0.4962171
## PKy        0.4662309               NA        0.4842810
## PKn        0.3519638               NA        0.4019362
## SKn        0.4723587               NA        0.4795632
## MKy        0.4772249               NA        0.4830570
## SKy        0.4507811               NA        0.4659924
## MKn        0.4555170               NA        0.4674966
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##      2085.054783       539.003152      2659.004719               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      4448.000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##               NA      1383.000000      1120.000000      2951.000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##      2617.000000      1392.000000      4448.000000      1392.000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##      5568.000000         3.383803         3.168061               NA 
## err.abs.trn.mean 
##         3.278544

## [1] "Features Importance for selected models:"

##                                All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Hhold.fctrPKn:.clusterid.fctr2             100.00000           100.0000000
## Hhold.fctrMKn:.clusterid.fctr2              81.77328            88.8670214
## Hhold.fctrSKn:.clusterid.fctr3              66.71128            69.4722329
## Hhold.fctrMKy:.clusterid.fctr3              59.34710            60.2954148
## Hhold.fctrSKy:.clusterid.fctr3              57.02139            65.1565192
## Hhold.fctrPKn                               51.32067            49.8597256
## Hhold.fctrMKn:.clusterid.fctr3              45.09956            41.4691537
## Hhold.fctrPKy:.clusterid.fctr3              45.08208            37.7793337
## Hhold.fctrMKy:.clusterid.fctr2              29.85346            44.4497672
## Hhold.fctrMKn:.clusterid.fctr4              27.36395            27.2337648
## Gender.fctrM                                22.12962            21.6949570
## Hhold.fctrMKy                               17.91030            11.6648978
## YOB.Age.fctr.L                              17.30492             7.2301676
## Hhold.fctrN:.clusterid.fctr3                15.26804            11.6568962
## Edn.fctr^4                                  14.82498             0.4712513
## Hhold.fctrPKn:.clusterid.fctr4              13.48564             0.0000000
## Edn.fctr^7                                  11.92495             1.2865952
## Income.fctr.C                               10.46973             6.2365025
## Gender.fctrF                                 0.00000            10.7286654

## [1] "glbObsNew prediction stats:"

## 
##    R    D 
## 1383    9

##                   label step_major step_minor label_minor     bgn     end
## 10     predict.data.new          6          0           0 348.799 362.386
## 11 display.session.info          7          0           0 362.387      NA
##    elapsed
## 10  13.587
## 11      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                      label step_major step_minor label_minor     bgn
## 1             cluster.data          1          0           0   8.995
## 2  partition.data.training          2          0           0 131.868
## 4               fit.models          4          0           0 243.475
## 5               fit.models          4          1           1 283.125
## 10        predict.data.new          6          0           0 348.799
## 6               fit.models          4          2           2 313.514
## 9        fit.data.training          5          1           1 339.638
## 8        fit.data.training          5          0           0 330.761
## 7               fit.models          4          3           3 326.019
## 3          select.features          3          0           0 240.288
##        end elapsed duration
## 1  131.868 122.873  122.873
## 2  240.287 108.420  108.419
## 4  283.124  39.650   39.649
## 5  313.514  30.389   30.389
## 10 362.386  13.587   13.587
## 6  326.018  12.504   12.504
## 9  348.798   9.160    9.160
## 8  339.637   8.877    8.876
## 7  330.760   4.741    4.741
## 3  243.474   3.186    3.186
## [1] "Total Elapsed Time: 362.386 secs"